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accelerometer</title> <meta name="description" content="Search results for: accelerometer"> <meta name="keywords" content="accelerometer"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="accelerometer"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 88</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: accelerometer</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">88</span> Development of MEMS Based 3-Axis Accelerometer for Hand Movement Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Aziz%20Ali%20Manjiyani">Zohra Aziz Ali Manjiyani</a>, <a href="https://publications.waset.org/abstracts/search?q=Renju%20Thomas%20Jacob"> Renju Thomas Jacob</a>, <a href="https://publications.waset.org/abstracts/search?q=Keerthan%20Kumar"> Keerthan Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This project develops a hand movement monitoring system, which feeds the data into the computer and gives the 3D image rotation according to the direction of the tilt and hence monitoring the movement of the hand in context to its tilt. Advancement of MEMS Technology has enabled us to get very small and low-cost accelerometer ICs which is based on capacitive principle. Accelerometer based Tilt sensor ADXL335 is used in this paper, based on MEMS technology and the project emphasis on the development of the MEMS-based accelerometer to measure the tilt, interfacing the hardware with the LabVIEW and showing the 3D rotation to the user, which is in his understandable form and tilt data can be saved in the computer. It provides an experience of working on emerging technologies like MEMS and design software like LabVIEW. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MEMS%20accelerometer" title="MEMS accelerometer">MEMS accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=tilt%20sensor%20ADXL335" title=" tilt sensor ADXL335"> tilt sensor ADXL335</a>, <a href="https://publications.waset.org/abstracts/search?q=LabVIEW%20simulation" title=" LabVIEW simulation"> LabVIEW simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20animation" title=" 3D animation"> 3D animation</a> </p> <a href="https://publications.waset.org/abstracts/5681/development-of-mems-based-3-axis-accelerometer-for-hand-movement-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">87</span> Calibration of the Radical Installation Limit Error of the Accelerometer in the Gravity Gradient Instrument</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danni%20Cong">Danni Cong</a>, <a href="https://publications.waset.org/abstracts/search?q=Meiping%20Wu"> Meiping Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaofeng%20He"> Xiaofeng He</a>, <a href="https://publications.waset.org/abstracts/search?q=Junxiang%20Lian"> Junxiang Lian</a>, <a href="https://publications.waset.org/abstracts/search?q=Juliang%20Cao"> Juliang Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaokuncai"> Shaokuncai</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Qin"> Hao Qin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gravity gradient instrument (GGI) is the core of the gravity gradiometer, so the structural error of the sensor has a great impact on the measurement results. In order not to affect the aimed measurement accuracy, limit error is required in the installation of the accelerometer. In this paper, based on the established measuring principle model, the radial installation limit error is calibrated, which is taken as an example to provide a method to calculate the other limit error of the installation under the premise of ensuring the accuracy of the measurement result. This method provides the idea for deriving the limit error of the geometry structure of the sensor, laying the foundation for the mechanical precision design and physical design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gravity%20gradient%20sensor" title="gravity gradient sensor">gravity gradient sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20installation%20limit%20error" title=" radial installation limit error"> radial installation limit error</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=uniaxial%20rotational%20modulation" title=" uniaxial rotational modulation"> uniaxial rotational modulation</a> </p> <a href="https://publications.waset.org/abstracts/75475/calibration-of-the-radical-installation-limit-error-of-the-accelerometer-in-the-gravity-gradient-instrument" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75475.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">422</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">86</span> Cancellation of Transducer Effects from Frequency Response Functions: Experimental Case Study on the Steel Plate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Zamani">P. Zamani</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Taleshi%20Anbouhi"> A. Taleshi Anbouhi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Ashory"> M. R. Ashory</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mohajerzadeh"> S. Mohajerzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Khatibi"> M. M. Khatibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modal analysis is a developing science in the experimental evaluation of dynamic properties of the structures. Mechanical devices such as accelerometers are one of the sources of lack of quality in measuring modal testing parameters. In this paper, eliminating the accelerometer’s mass effect of the frequency response of the structure is studied. So, a strategy is used for eliminating the mass effect by using sensitivity analysis. In this method, the amount of mass change and the place to measure the structure’s response with least error in frequency correction is chosen. Experimental modal testing is carried out on a steel plate and the effect of accelerometer’s mass is omitted using this strategy. Finally, a good agreement is achieved between numerical and experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer%20mass" title="accelerometer mass">accelerometer mass</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20response%20function" title=" frequency response function"> frequency response function</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20analysis" title=" modal analysis"> modal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a> </p> <a href="https://publications.waset.org/abstracts/29375/cancellation-of-transducer-effects-from-frequency-response-functions-experimental-case-study-on-the-steel-plate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">85</span> Autonomous Position Control of an Unmanned Aerial Vehicle Based on Accelerometer Response for Indoor Navigation Using Kalman Filtering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Misbahuddin">Syed Misbahuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sagufta%20Kapadia"> Sagufta Kapadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous indoor drone navigation has been posed with various challenges, including the inability to use a Global Positioning System (GPS). As of now, Unmanned Aerial Vehicles (UAVs) either rely on 3D mapping systems or utilize external camera arrays to track the UAV in an enclosed environment. The objective of this paper is to develop an algorithm that utilizes Kalman Filtering to reduce noise, allowing the UAV to be navigated indoors using only the flight controller and an onboard companion computer. In this paper, open-source libraries are used to control the UAV, which will only use the onboard accelerometer on the flight controller to estimate the position through double integration. One of the advantages of such a system is that it allows for low-cost and lightweight UAVs to autonomously navigate indoors without advanced mapping of the environment or the use of expensive high-precision-localization sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor-navigation" title=" indoor-navigation"> indoor-navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman-filtering" title=" Kalman-filtering"> Kalman-filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=position-control" title=" position-control "> position-control </a> </p> <a href="https://publications.waset.org/abstracts/115917/autonomous-position-control-of-an-unmanned-aerial-vehicle-based-on-accelerometer-response-for-indoor-navigation-using-kalman-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">84</span> Design and Simulation Interface Circuit for Piezoresistive Accelerometers with Offset Cancellation Ability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Bagheri">Mohsen Bagheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Afifi"> Ahmad Afifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new method for read out of the piezoresistive accelerometer sensors. The circuit works based on instrumentation amplifier and it is useful for reducing offset in Wheatstone bridge. The obtained gain is 645 with 1 μv/°c equivalent drift and 1.58 mw power consumption. A Schmitt trigger and multiplexer circuit control output node. A high speed counter is designed in this work. The proposed circuit is designed and simulated in 0.18 μm CMOS technology with 1.8 v power supply. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piezoresistive%20accelerometer" title="piezoresistive accelerometer">piezoresistive accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=zero%20offset" title=" zero offset"> zero offset</a>, <a href="https://publications.waset.org/abstracts/search?q=Schmitt%20trigger" title=" Schmitt trigger"> Schmitt trigger</a>, <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20reversible%20counter" title=" bidirectional reversible counter"> bidirectional reversible counter</a> </p> <a href="https://publications.waset.org/abstracts/6238/design-and-simulation-interface-circuit-for-piezoresistive-accelerometers-with-offset-cancellation-ability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6238.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">83</span> Comparison of Dynamic Characteristics of Railway Bridge Spans to Know the Health of Elastomeric Bearings Using Tri Axial Accelerometer Sensors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Narayanakumar%20Somasundaram">Narayanakumar Somasundaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkat%20Nihit%20Chirivella"> Venkat Nihit Chirivella</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkata%20Dilip%20Kumar%20Pasupuleti"> Venkata Dilip Kumar Pasupuleti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ajakool, India, has a multi-span bridge that is constructed for rail transport with a maximum operating speed of 100 km/hr. It is a standard RDSO design of a PSC box girder carrying a single railway track. The Structural Health Monitoring System (SHM) is designed and installed to compare and analyze the vibrations and displacements on the bridge due to different live loads from moving trains. The study is conducted for three different spans of the same bridge to understand the health of the elastomeric bearings. Also, to validate the same, a three-dimensional finite element model is developed, and modal analysis is carried out. The proposed methodology can help in detecting deteriorated elastomeric bearings using only wireless tri-accelerometer sensors. Detailed analysis and results are presented in terms of mode shapes, accelerations, displacements, and their importance to each other. This can be implemented with a lot of ease and can be more accurate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20effects" title="dynamic effects">dynamic effects</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20analysis" title=" vibration analysis"> vibration analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer%20sensors" title=" accelerometer sensors"> accelerometer sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=elastomeric%20bearing" title=" elastomeric bearing"> elastomeric bearing</a> </p> <a href="https://publications.waset.org/abstracts/154069/comparison-of-dynamic-characteristics-of-railway-bridge-spans-to-know-the-health-of-elastomeric-bearings-using-tri-axial-accelerometer-sensors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">82</span> Using Probe Person Data for Travel Mode Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Awais%20Shafique">Muhammad Awais Shafique</a>, <a href="https://publications.waset.org/abstracts/search?q=Eiji%20Hato"> Eiji Hato</a>, <a href="https://publications.waset.org/abstracts/search?q=Hideki%20Yaginuma"> Hideki Yaginuma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=AdaBoost" title=" AdaBoost"> AdaBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a>, <a href="https://publications.waset.org/abstracts/search?q=mode%20prediction" title=" mode prediction"> mode prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/13792/using-probe-person-data-for-travel-mode-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13792.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">81</span> Implant Operation Guiding Device for Dental Surgeons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Hyun">Daniel Hyun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dental implants are one of the top 3 reasons to sue a dentist for malpractice. It involves dental implant complications, usually because of the angle of the implant from the surgery. At present, surgeons usually use a 3D-printed navigator that is customized for the patient’s teeth. However, those can’t be reused for other patients as they require time. Therefore, I made a guiding device to assist the surgeon in implant operations. The surgeon can input the objective of the operation, and the device constantly checks if the surgery is heading towards the objective within the set range, telling the surgeon by manipulating the LED. We tested the prototypes’ consistency and accuracy by checking the graph, average standard deviation, and the average change of the calculated angles. The accuracy of performance was also acquired by running the device and checking the outputs. My first prototype used accelerometer and gyroscope sensors from the Arduino MPU6050 sensor, getting a changeable graph, achieving 0.0295 of standard deviations, 0.25 of average change, and 66.6% accuracy of performance. The second prototype used only the gyroscope, and it got a constant graph, achieved 0.0062 of standard deviation, 0.075 of average change, and 100% accuracy of performance, indicating that the accelerometer sensor aggravated the functionality of the device. Using the gyroscope sensor allowed it to measure the orientations of separate axes without affecting each other and also increased the stability and accuracy of the measurements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=implant" title="implant">implant</a>, <a href="https://publications.waset.org/abstracts/search?q=guide" title=" guide"> guide</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=gyroscope" title=" gyroscope"> gyroscope</a>, <a href="https://publications.waset.org/abstracts/search?q=handpiece" title=" handpiece"> handpiece</a> </p> <a href="https://publications.waset.org/abstracts/187019/implant-operation-guiding-device-for-dental-surgeons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187019.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">80</span> A Systematic Review of Pedometer-or Accelerometer-Based Interventions for Increasing Physical Activity in Low Socioeconomic Groups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaun%20G.%20Abbott">Shaun G. Abbott</a>, <a href="https://publications.waset.org/abstracts/search?q=Rebecca%20C.%20Reynolds"> Rebecca C. Reynolds</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20B.%20Etter"> James B. Etter</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20B.%20F.%20de%20Wit"> John B. F. de Wit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The benefits of physical activity (PA) on health are well documented. Low socioeconomic status (SES) is associated with poor health, with PA a suggested mediator. Pedometers and accelerometers offer an effective behavior change tool to increase PA levels. While the role of pedometer and accelerometer use in increasing PA is recognized in many populations, little is known in low-SES groups. We are aiming to assess the effectiveness of pedometer- and accelerometer-based interventions for increasing PA step count and improving subsequent health outcomes among low-SES groups of high-income countries. Medline, Embase, PsycINFO, CENTRAL and SportDiscus databases were searched to identify articles published before 10th July, 2015; using search terms developed from previous systematic reviews. Inclusion criteria are: low-SES participants classified by income, geography, education, occupation or ethnicity; study duration minimum 4 weeks; an intervention and control group; wearing of an unsealed pedometer or accelerometer to objectively measure PA as step counts per day for the duration of the study. We retrieved 2,142 articles from our database searches, after removal of duplicates. Two investigators independently reviewed titles and abstracts of these articles (50% each) and a combined 20% sample were reviewed to account for inter-assessor variation. We are currently verifying the full texts of 430 articles. Included studies will be critically appraised for risk of bias using guidelines suggested by the Cochrane Public Health Group. Two investigators will extract data concerning the intervention; study design; comparators; steps per day; participants; context and presence or absence of obesity and/or chronic disease. Heterogeneity amongst studies is anticipated, thus a narrative synthesis of data will be conducted with the simplification of selected results into percentage increases from baseline to allow for between-study comparison. Results will be presented at the conference in December if selected. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=pedometer" title=" pedometer"> pedometer</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20activity" title=" physical activity"> physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=socioeconomic" title=" socioeconomic"> socioeconomic</a>, <a href="https://publications.waset.org/abstracts/search?q=step%20count" title=" step count"> step count</a> </p> <a href="https://publications.waset.org/abstracts/39369/a-systematic-review-of-pedometer-or-accelerometer-based-interventions-for-increasing-physical-activity-in-low-socioeconomic-groups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">79</span> Optimising Participation in Physical Activity Research for Adults with Intellectual Disabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yetunde%20M.%20Dairo">Yetunde M. Dairo</a>, <a href="https://publications.waset.org/abstracts/search?q=Johnny%20Collett"> Johnny Collett</a>, <a href="https://publications.waset.org/abstracts/search?q=Helen%20Dawes"> Helen Dawes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background and Aim: Engagement with physical activity (PA) research is poor among adults with intellectual disabilities (ID), particularly in those from residential homes. This study explored why, by asking managers of residential homes, adults with ID and their carers. Methods: Participants: A convenient sample of 23 individuals from two UK local authorities, including a group of ID residential home managers, adults with ID and their support staff. Procedures: A) Residential home managers (n=6) were asked questions about their willingness to allow their residents to participate in PA research; B) eleven adults with ID and their support workers (n=6) were asked questions about their willingness to accept 7-day accelerometer monitoring and/or the International Physical Activity Questionnaire-short version (IPAQ-s) as PA measures. The IPAQ-s was administered by the researcher and they were each provided with samples of accelerometers to try on. Results: A) Five out of six managers said that the burden of wearing the accelerometer for seven days would be too high for the people they support, the majority of whom might be unable to express their wishes. They also said they would be unwilling to act as proxy respondents for the same reason. Additionally, they cited time pressure, understaffing, and reluctance to spend time on the research paperwork as further reasons for non-participation. B) All 11 individuals with ID completed the IPAQ-s while only three accepted the accelerometer, one of whom was deemed inappropriate to wear it. Reasons for rejecting accelerometers included statements from participants of: ‘too expensive’, ‘too heavy’, ‘uncomfortable’, and two people said they would not want to wear it for more than one day. All adults with ID (11) and their support workers (6) provided information about their physical activity levels through the IPAQ-s. Conclusions: Care home managers are a barrier to research participation. However, adults with ID would be happy for the IPAQ-s as a PA measure, but less so for the 7-day accelerometer monitoring. In order to improve participation in this population, the choice of PA measure is considered important. Moreover, there is a need for studies exploring how best to engage ID residential home managers in PA research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intellectual%20disability" title="intellectual disability">intellectual disability</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20activity%20measurement" title=" physical activity measurement"> physical activity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20engagement" title=" research engagement"> research engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20participation" title=" research participation"> research participation</a> </p> <a href="https://publications.waset.org/abstracts/59083/optimising-participation-in-physical-activity-research-for-adults-with-intellectual-disabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59083.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> Compact Optical Sensors for Harsh Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Branislav%20Timotijevic">Branislav Timotijevic</a>, <a href="https://publications.waset.org/abstracts/search?q=Yves%20Petremand"> Yves Petremand</a>, <a href="https://publications.waset.org/abstracts/search?q=Markus%20Luetzelschwab"> Markus Luetzelschwab</a>, <a href="https://publications.waset.org/abstracts/search?q=Dara%20Bayat"> Dara Bayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Laurent%20Aebi"> Laurent Aebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical miniaturized sensors with remote readout are required devices for the monitoring in harsh electromagnetic environments. As an example, in turbo and hydro generators, excessively high vibrations of the end-windings can lead to dramatic damages, imposing very high, additional service costs. A significant change of the generator temperature can also be an indicator of the system failure. Continuous monitoring of vibrations, temperature, humidity, and gases is therefore mandatory. The high electromagnetic fields in the generators impose the use of non-conductive devices in order to prevent electromagnetic interferences and to electrically isolate the sensing element to the electronic readout. Metal-free sensors are good candidates for such systems since they are immune to very strong electromagnetic fields and given the fact that they are non-conductive. We have realized miniature optical accelerometer and temperature sensors for a remote sensing of the harsh environments using the common, inexpensive silicon Micro Electro-Mechanical System (MEMS) platform. Both devices show highly linear response. The accelerometer has a deviation within 1% from the linear fit when tested in a range 0 – 40 g. The temperature sensor can provide the measurement accuracy better than 1 °C in a range 20 – 150 °C. The design of other type of sensors for the environments with high electromagnetic interferences has also been discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20MEMS" title="optical MEMS">optical MEMS</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20sensor" title=" temperature sensor"> temperature sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=harsh%20environment" title=" harsh environment"> harsh environment</a> </p> <a href="https://publications.waset.org/abstracts/65332/compact-optical-sensors-for-harsh-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">77</span> Development of a BriMAIN System for Health Monitoring of Railway Bridges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prakher%20Mishra">Prakher Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Dikshant%20Bodana"> Dikshant Bodana</a>, <a href="https://publications.waset.org/abstracts/search?q=Saloni%20Desai"> Saloni Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhanshu%20Dixit"> Sudhanshu Dixit</a>, <a href="https://publications.waset.org/abstracts/search?q=Sopan%20Agarwal"> Sopan Agarwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Shriraj%20Patel"> Shriraj Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Railways are sometimes lifeline of nations as they consist of huge network of rail lines and bridges. Reportedly many of the bridges are aging, weak, distressed and accident prone. It becomes a really challenging task for Engineers and workers to keep up a regular maintenance schedule for proper functioning which itself is quite a hard hitting job. In this paper we have come up with an innvovative wireless system of maintenance called BriMAIN. In this system we have installed two types of sensors, first one is called a force sensor which will continously analyse the readings of pressure at joints of the bridges and secondly an MPU-6050 triaxial gyroscope+accelerometer which will analyse the deflection of the deck of the bridge. Apart from this a separate database is also being made at the server room so that the data can be visualized by the engineers and a warning can be issued in case reading of the sensors goes above threshold. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Accelerometer" title="Accelerometer">Accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=B-MAIN" title=" B-MAIN"> B-MAIN</a>, <a href="https://publications.waset.org/abstracts/search?q=Gyroscope" title=" Gyroscope"> Gyroscope</a>, <a href="https://publications.waset.org/abstracts/search?q=MPU-6050" title=" MPU-6050"> MPU-6050</a> </p> <a href="https://publications.waset.org/abstracts/77940/development-of-a-brimain-system-for-health-monitoring-of-railway-bridges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77940.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">76</span> Investigating Activity Recognition Using 9-Axis Sensors and Filters in Wearable Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Gil%20Ahn">Jun Gil Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Kang%20Park"> Jong Kang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=activity%20recognition" title=" activity recognition"> activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=directiona%20cosine%20matrix%20filter" title=" directiona cosine matrix filter"> directiona cosine matrix filter</a>, <a href="https://publications.waset.org/abstracts/search?q=gyroscope" title=" gyroscope"> gyroscope</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetometer" title=" magnetometer"> magnetometer</a> </p> <a href="https://publications.waset.org/abstracts/56198/investigating-activity-recognition-using-9-axis-sensors-and-filters-in-wearable-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">75</span> Retrofitted Semi-Active Suspension System for a Eelectric Model Vehicle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiuh-Jer%20Huang">Shiuh-Jer Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Han%20Yeh"> Yun-Han Yeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A 40 steps manual adjusting shock absorber was refitted with DC motor driving mechanism to construct as a semi-active suspension system for a four-wheel drive electric vehicle. Accelerometer and potentiometer sensors are installed to measure the sprung mass acceleration and suspension system compression or rebound states for control purpose. A fuzzy logic controller was designed to derive appropriate damping target based on vehicle running condition for semi-active suspension system to follow. The damping ratio control of each wheel axis suspension system is executed with a robust fuzzy sliding mode controller (FSMC). Different road surface conditions are chosen to evaluate the control performance of this semi-active suspension system based on wheel axis acceleration signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-active%20suspension" title="semi-active suspension">semi-active suspension</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title=" electric vehicle"> electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sliding%20mode%20control" title=" fuzzy sliding mode control"> fuzzy sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a> </p> <a href="https://publications.waset.org/abstracts/17661/retrofitted-semi-active-suspension-system-for-a-eelectric-model-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17661.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> Development of a Low-Cost Smart Insole for Gait Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Khairul%20Halim">S. M. Khairul Halim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Ghodsi"> Mojtaba Ghodsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Morteza%20Mohammadzaheri"> Morteza Mohammadzaheri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gait analysis is essential for diagnosing musculoskeletal and neurological conditions. However, current methods are often complex and expensive. This paper introduces a methodology for analysing gait parameters using a smart insole with a built-in accelerometer. The system measures stance time, swing time, step count, and cadence and wirelessly transmits data to a user-friendly IoT dashboard for centralized processing. This setup enables remote monitoring and advanced data analytics, making it a versatile tool for medical diagnostics and everyday usage. Integration with IoT enhances the portability and connectivity of the device, allowing for secure, encrypted data access over the Internet. This feature supports telemedicine and enables personalized treatment plans tailored to individual needs. Overall, the approach provides a cost-effective (almost 25 GBP), accurate, and user-friendly solution for gait analysis, facilitating remote tracking and customized therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gait%20analysis" title="gait analysis">gait analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20insole" title=" smart insole"> smart insole</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer%20sensor" title=" accelerometer sensor"> accelerometer sensor</a> </p> <a href="https://publications.waset.org/abstracts/192566/development-of-a-low-cost-smart-insole-for-gait-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">73</span> A Smartphone-Based Real-Time Activity Recognition and Fall Detection System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manutchanok%20Jongprasithporn">Manutchanok Jongprasithporn</a>, <a href="https://publications.waset.org/abstracts/search?q=Rawiphorn%20Srivilai"> Rawiphorn Srivilai</a>, <a href="https://publications.waset.org/abstracts/search?q=Paweena%20Pongsopha"> Paweena Pongsopha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fall is the most serious accident leading to increased unintentional injuries and mortality. Falls are not only the cause of suffering and functional impairments to the individuals, but also the cause of increasing medical cost and days away from work. The early detection of falls could be an advantage to reduce fall-related injuries and consequences of falls. Smartphones, embedded accelerometer, have become a common device in everyday life due to decreasing technology cost. This paper explores a physical activity monitoring and fall detection application in smartphones which is a non-invasive biomedical device to determine physical activities and fall event. The combination of application and sensors could perform as a biomedical sensor to monitor physical activities and recognize a fall. We have chosen Android-based smartphone in this study since android operating system is an open-source and no cost. Moreover, android phone users become a majority of Thai’s smartphone users. We developed Thai 3 Axis (TH3AX) as a physical activities and fall detection application which included command, manual, results in Thai language. The smartphone was attached to right hip of 10 young, healthy adult subjects (5 males, 5 females; aged< 35y) to collect accelerometer and gyroscope data during performing physical activities (e.g., walking, running, sitting, and lying down) and falling to determine threshold for each activity. Dependent variables are including accelerometer data (acceleration, peak acceleration, average resultant acceleration, and time between peak acceleration). A repeated measures ANOVA was performed to test whether there are any differences between DVs’ means. Statistical analyses were considered significant at p<0.05. After finding threshold, the results were used as training data for a predictive model of activity recognition. In the future, accuracies of activity recognition will be performed to assess the overall performance of the classifier. Moreover, to help improve the quality of life, our system will be implemented with patients and elderly people who need intensive care in hospitals and nursing homes in Thailand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activity%20recognition" title="activity recognition">activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=fall" title=" fall"> fall</a>, <a href="https://publications.waset.org/abstracts/search?q=gyroscope" title=" gyroscope"> gyroscope</a>, <a href="https://publications.waset.org/abstracts/search?q=smartphone" title=" smartphone "> smartphone </a> </p> <a href="https://publications.waset.org/abstracts/27452/a-smartphone-based-real-time-activity-recognition-and-fall-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27452.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">692</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">72</span> Monitor Vehicle Speed Using Internet of Things Based Wireless Sensor Network System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akber%20Oumer%20Abdurezak">Akber Oumer Abdurezak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road traffic accident is a major problem in Ethiopia, resulting in the deaths of many people and potential injuries and crash every year and loss of properties. According to the Federal Transport Authority, one of the main causes of traffic accident and crash in Ethiopia is over speeding. Implementation of different technologies is used to monitor the speed of vehicles in order to minimize accidents and crashes. This research aimed at designing a speed monitoring system to monitor the speed of travelling vehicles and movements, reporting illegal speeds or overspeeding vehicles to the concerned bodies. The implementation of the system is through a wireless sensor network. The proposed system can sense and detect the movement of vehicles, process, and analysis the data obtained from the sensor and the cloud system. The data is sent to the central controlling server. The system contains accelerometer and gyroscope sensors to sense and collect the data of the vehicle. Arduino to process the data and Global System for Mobile Communication (GSM) module for communication purposes to send the data to the concerned body. When the speed of the vehicle exceeds the allowable speed limit, the system sends a message to database as “over speeding”. Both accelerometer and gyroscope sensors are used to collect acceleration data. The acceleration data then convert to speed, and the corresponding speed is checked with the speed limit, and those above the speed limit are reported to the concerned authorities to avoid frequent accidents. The proposed system decreases the occurrence of accidents and crashes due to overspeeding and can be used as an eye opener for the implementation of other intelligent transport system technologies. This system can also integrate with other technologies like GPS and Google Maps to obtain better output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=IOT" title=" IOT"> IOT</a>, <a href="https://publications.waset.org/abstracts/search?q=GSM" title=" GSM"> GSM</a>, <a href="https://publications.waset.org/abstracts/search?q=gyroscope" title=" gyroscope"> gyroscope</a> </p> <a href="https://publications.waset.org/abstracts/163129/monitor-vehicle-speed-using-internet-of-things-based-wireless-sensor-network-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163129.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">71</span> An eHealth Intervention Using Accelerometer- Smart Phone-App Technology to Promote Physical Activity and Health among Employees in a Military Setting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emilia%20Pietil%C3%A4inen">Emilia Pietiläinen</a>, <a href="https://publications.waset.org/abstracts/search?q=Heikki%20Kyr%C3%B6l%C3%A4inen"> Heikki Kyröläinen</a>, <a href="https://publications.waset.org/abstracts/search?q=Tommi%20Vasankari"> Tommi Vasankari</a>, <a href="https://publications.waset.org/abstracts/search?q=Matti%20Santtila"> Matti Santtila</a>, <a href="https://publications.waset.org/abstracts/search?q=Tiina%20Luukkaala"> Tiina Luukkaala</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Parkkola"> Kai Parkkola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Working in the military sets special demands on physical fitness, however, reduced physical activity levels among employees in the Finnish Defence Forces (FDF), a trend also being seen among the working-age population in Finland, is leading to reduced physical fitness levels and increased risk of cardiovascular and metabolic diseases, something which also increases human resource costs. Therefore, the aim of the present study was to develop an eHealth intervention using accelerometer- smartphone app feedback technique, telephone counseling and physical activity recordings to increase physical activity of the personnel and thereby improve their health. Specific aims were to reduce stress, improve quality of sleep and mental and physical performance, ability to work and reduce sick leave absences. Employees from six military brigades around Finland were invited to participate in the study, and finally, 260 voluntary participants were included (66 women, 194 men). The participants were randomized into intervention (156) and control groups (104). The eHealth intervention group used accelerometers measuring daily physical activity and duration and quality of sleep for six months. The accelerometers transmitted the data to smartphone apps while giving feedback about daily physical activity and sleep. The intervention group participants were also encouraged to exercise for two hours a week during working hours, a benefit that was already offered to employees following existing FDF guidelines. To separate the exercise done during working hours from the accelerometer data, the intervention group marked this exercise into an exercise diary. The intervention group also participated in telephone counseling about their physical activity. On the other hand, the control group participants continued with their normal exercise routine without the accelerometer and feedback. They could utilize the benefit of being able to exercise during working hours, but they were not separately encouraged for it, nor was the exercise diary used. The participants were measured at baseline, after the entire intervention period, and six months after the end of the entire intervention. The measurements included accelerometer recordings, biochemical laboratory tests, body composition measurements, physical fitness tests, and a wide questionnaire focusing on sociodemographic factors, physical activity and health. In terms of results, the primary indicators of effectiveness are increased physical activity and fitness, improved health status, and reduced sick leave absences. The evaluation of the present scientific reach is based on the data collected during the baseline measurements. Maintenance of the studied outcomes is assessed by comparing the results of the control group measured at the baseline and a year follow-up. Results of the study are not yet available but will be presented at the conference. The present findings will help to develop an easy and cost-effective model to support the health and working capability of employees in the military and other workplaces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=health" title=" health"> health</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20applications" title=" mobile applications"> mobile applications</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20activity" title=" physical activity"> physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20performance" title=" physical performance"> physical performance</a> </p> <a href="https://publications.waset.org/abstracts/141915/an-ehealth-intervention-using-accelerometer-smart-phone-app-technology-to-promote-physical-activity-and-health-among-employees-in-a-military-setting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141915.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> Impact Force Difference on Natural Grass Versus Synthetic Turf Football Fields</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nathaniel%20C.%20Villanueva">Nathaniel C. Villanueva</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20K.%20H.%20Chun"> Ian K. H. Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Alyssa%20S.%20Fujiwara"> Alyssa S. Fujiwara</a>, <a href="https://publications.waset.org/abstracts/search?q=Emily%20R.%20Leibovitch"> Emily R. Leibovitch</a>, <a href="https://publications.waset.org/abstracts/search?q=Brennan%20E.%20Yamamoto"> Brennan E. Yamamoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Loren%20G.%20Yamamoto"> Loren G. Yamamoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: In previous studies of high school sports, over 15% of concussions were attributed to contact with the playing surface. While artificial turf fields are increasing in popularity due to lower maintenance costs, artificial turf has been associated with more ankle and knee injuries, with inconclusive data on concussions. In this study, natural grass and artificial football fields were compared in terms of deceleration on fall impact. Methods: Accelerometers were placed on the forehead, apex of the head, and right ear of a Century Body Opponent Bag (BOB) manikin. A Riddell HITS football helmet was secured onto the head of the manikin over the accelerometers. This manikin was dropped onto natural grass (n = 10) and artificial turf (n = 9) high school football fields. The manikin was dropped from a stationary position at a height of 60 cm onto its front, back, and left side. Each of these drops was conducted 10 times at the 40-yard line, 20-yard line, and endzone. The net deceleration on impact was calculated as a net vector from each of the three accelerometers’ x, y, and z vectors from the three different locations on the manikin’s head (9 vector measurements per drop). Results: Mean values for the multiple drops were calculated for each accelerometer and drop type for each field. All accelerometers in forward and backward falls and one accelerometer in side falls showed significantly greater impact force on synthetic turf compared to the natural grass surfaces. Conclusion: Impact force was higher on synthetic fields for all drop types for at least one of the accelerometer locations. These findings suggest that concussion risk might be higher for athletes playing on artificial turf fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concussion" title="concussion">concussion</a>, <a href="https://publications.waset.org/abstracts/search?q=football" title=" football"> football</a>, <a href="https://publications.waset.org/abstracts/search?q=biomechanics" title=" biomechanics"> biomechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=sports" title=" sports"> sports</a> </p> <a href="https://publications.waset.org/abstracts/147563/impact-force-difference-on-natural-grass-versus-synthetic-turf-football-fields" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">69</span> Detect Cable Force of Cable Stayed Bridge from Accelerometer Data of SHM as Real Time</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Lan">Nguyen Lan</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Tan%20Kien"> Le Tan Kien</a>, <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Pham%20Gia%20Bao"> Nguyen Pham Gia Bao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cable-stayed bridge belongs to the combined system, in which the cables is a major strutual element. Cable-stayed bridges with large spans are often arranged with structural health monitoring systems to collect data for bridge health diagnosis. Cables tension monitoring is a structural monitoring content. It is common to measure cable tension by a direct force sensor or cable vibration accelerometer sensor, thereby inferring the indirect cable tension through the cable vibration frequency. To translate cable-stayed vibration acceleration data to real-time tension requires some necessary calculations and programming. This paper introduces the algorithm, labview program that converts cable-stayed vibration acceleration data to real-time tension. The research results are applied to the monitoring system of Tran Thi Ly cable-stayed bridge and Song Hieu cable-stayed bridge in Vietnam. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cable-stayed%20bridge" title="cable-stayed bridge">cable-stayed bridge</a>, <a href="https://publications.waset.org/abstracts/search?q=cable%20fore" title=" cable fore"> cable fore</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20heath%20monitoring%20%28SHM%29" title=" structural heath monitoring (SHM)"> structural heath monitoring (SHM)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20fourie%20transformed%20%28FFT%29" title=" fast fourie transformed (FFT)"> fast fourie transformed (FFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time" title=" real time"> real time</a>, <a href="https://publications.waset.org/abstracts/search?q=vibrations" title=" vibrations"> vibrations</a> </p> <a href="https://publications.waset.org/abstracts/182663/detect-cable-force-of-cable-stayed-bridge-from-accelerometer-data-of-shm-as-real-time" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182663.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">68</span> Advancing the Analysis of Physical Activity Behaviour in Diverse, Rapidly Evolving Populations: Using Unsupervised Machine Learning to Segment and Cluster Accelerometer Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Thornton">Christopher Thornton</a>, <a href="https://publications.waset.org/abstracts/search?q=Niina%20Kolehmainen"> Niina Kolehmainen</a>, <a href="https://publications.waset.org/abstracts/search?q=Kianoush%20Nazarpour"> Kianoush Nazarpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physical%20activity" title="physical activity">physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=under%205s" title=" under 5s"> under 5s</a>, <a href="https://publications.waset.org/abstracts/search?q=disability" title=" disability"> disability</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a> </p> <a href="https://publications.waset.org/abstracts/139588/advancing-the-analysis-of-physical-activity-behaviour-in-diverse-rapidly-evolving-populations-using-unsupervised-machine-learning-to-segment-and-cluster-accelerometer-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">210</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">67</span> The Relationships between Physical Activity Levels, Enjoyment of Physical Activity, and Body Mass Index among Bruneian Secondary School Adolescents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Xiaoqian%20Sun">David Xiaoqian Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Khairunnisa%20Binti%20Haji%20Sibah"> Khairunnisa Binti Haji Sibah</a>, <a href="https://publications.waset.org/abstracts/search?q=Jr."> Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lejak%20Anak%20Ambol"> Lejak Anak Ambol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the study was to examine the relationships between objectively measured physical activity levels (PALs), enjoyment of physical activity (EPA), and body mass index (BMI) among adolescents. A total of 188 12-14-year-old Bruneian secondary school adolescents (88 boys and 100 girls) voluntarily took part in this study. Subjects wore the RT3 accelerometer for seven consecutive days in order to measure their PALs. Times of students’ engagement in total (TPA), light (LPA), moderate (MPV), and vigorous PA (VPA) were obtained from the accelerometer. Their BMIs were calculated from their body height and weight. Physical Activity Enjoyment Scale (PACES) was administrated to obtain their EPA levels. Four key enjoyment factors including fun factors, positive perceptions, unexciting in doing activities, and negative perceptions were identified. Subjects’ social economic status (SES) was provided by school administration. Results show that all the adolescents did not meet the recommended PA guidelines even though boys were engaged in more MVPA than girls. No relationships were found between BMI and all PALs in both boys and girls. BMI was significantly related to the PACES scores (r = -.22, p = 0.01), fun factors (r = -.20, p = 0.05) and positive perceptions (r =-.21, p < 0.05). The PACES scores were significantly related to LPA (r = .18, p = 0.01) but not related to MVPA (r = .04, p > 0.05). After controlling for age and SES, BMI was only significantly related to the PACES scores in girls (r = -.27, p < .01) but boys (r = -.06, p > 0.05). Fun factors were significantly related to LPA and MVPA (p < .01) in girls while negative perceptions were significantly related to LPA and MVPA (p < .01) in boys. This study provides evidence that enjoyment may be a trigger of LPA but MVPA and may be influenced by their BMI status particularly in girls. Based on these findings, physical and health educators are suggested to not only make PA more enjoyable, but also consider gender differences in promoting adolescents' participation in MVPA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20mass%20index" title=" body mass index"> body mass index</a>, <a href="https://publications.waset.org/abstracts/search?q=enjoyment%20of%20physical%20activity" title=" enjoyment of physical activity"> enjoyment of physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=moderate%20to%20vigorous%20physical%20activity" title=" moderate to vigorous physical activity"> moderate to vigorous physical activity</a> </p> <a href="https://publications.waset.org/abstracts/10247/the-relationships-between-physical-activity-levels-enjoyment-of-physical-activity-and-body-mass-index-among-bruneian-secondary-school-adolescents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10247.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">377</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">66</span> Analyzing the Street Pattern Characteristics on Young People’s Choice to Walk or Not: A Study Based on Accelerometer and Global Positioning Systems Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebru%20Cubukcu">Ebru Cubukcu</a>, <a href="https://publications.waset.org/abstracts/search?q=Gozde%20Eksioglu%20Cetintahra"> Gozde Eksioglu Cetintahra</a>, <a href="https://publications.waset.org/abstracts/search?q=Burcin%20Hepguzel%20Hatip"> Burcin Hepguzel Hatip</a>, <a href="https://publications.waset.org/abstracts/search?q=Mert%20Cubukcu"> Mert Cubukcu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Obesity and overweight cause serious health problems. Public and private organizations aim to encourage walking in various ways in order to cope with the problem of obesity and overweight. This study aims to understand how the spatial characteristics of urban street pattern, connectivity and complexity influence young people’s choice to walk or not. 185 public university students in Izmir, the third largest city in Turkey, participated in the study. Each participant had worn an accelerometer and a global positioning (GPS) device for a week. The accelerometer device records data on the intensity of the participant’s activity at a specified time interval, and the GPS device on the activities’ locations. Combining the two datasets, activity maps are derived. These maps are then used to differentiate the participants’ walk trips and motor vehicle trips. Given that, the frequency of walk and motor vehicle trips are calculated at the street segment level, and the street segments are then categorized into two as ‘preferred by pedestrians’ and ‘preferred by motor vehicles’. Graph Theory-based accessibility indices are calculated to quantify the spatial characteristics of the streets in the sample. Six different indices are used: (I) edge density, (II) edge sinuosity, (III) eta index, (IV) node density, (V) order of a node, and (VI) beta index. T-tests show that the index values for the ‘preferred by pedestrians’ and ‘preferred by motor vehicles’ are significantly different. The findings indicate that the spatial characteristics of the street network have a measurable effect on young people’s choice to walk or not. Policy implications are discussed. This study is funded by the Scientific and Technological Research Council of Turkey, Project No: 116K358. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title="graph theory">graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=walkability" title=" walkability"> walkability</a>, <a href="https://publications.waset.org/abstracts/search?q=accessibility" title=" accessibility"> accessibility</a>, <a href="https://publications.waset.org/abstracts/search?q=street%20network" title=" street network"> street network</a> </p> <a href="https://publications.waset.org/abstracts/89579/analyzing-the-street-pattern-characteristics-on-young-peoples-choice-to-walk-or-not-a-study-based-on-accelerometer-and-global-positioning-systems-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">226</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">65</span> Fiber Braggs Grating Sensor Based Instrumentation to Evaluate Postural Balance and Stability on an Unstable Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Chethana">K. Chethana</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20S.%20Guru%20Prasad"> A. S. Guru Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20N.%20Vikranth"> H. N. Vikranth</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Varun"> H. Varun</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20N.%20Omkar"> S. N. Omkar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Asokan"> S. Asokan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a novel application of Fiber Braggs Grating (FBG) sensors on an unstable platform to assess human postural stability and balance. The FBG sensor based Stability Analyzing Device (FBGSAD) developed demonstrates the applicability of FBG sensors in the measurement of plantar strain to assess the postural stability of subjects on unstable platforms during different stances in eyes open and eyes closed conditions on a rocker board. Comparing the Centre of Gravity (CG) variations measured on the lumbar vertebra of subjects using a commercial accelerometer along with FBGSAD validates the study. The results obtained depict qualitative similarities between the data recorded by both FBGSAD and accelerometer, illustrating the reliability and consistency of FBG sensors in biomechanical applications for both young and geriatric population. The developed FBGSAD simultaneously measures plantar strain distribution and postural stability and can serve as a tool/yardstick to mitigate space motion sickness, identify individuals who are susceptible to falls and to qualify subjects for balance and stability, which are important factors in the selection of certain unique professionals such as aircraft pilots, astronauts, cosmonauts etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomechanics" title="biomechanics">biomechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=fiber%20bragg%20gratings" title=" fiber bragg gratings"> fiber bragg gratings</a>, <a href="https://publications.waset.org/abstracts/search?q=plantar%20strain%20measurement" title=" plantar strain measurement"> plantar strain measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=postural%20stability%20analysis" title=" postural stability analysis"> postural stability analysis</a> </p> <a href="https://publications.waset.org/abstracts/20841/fiber-braggs-grating-sensor-based-instrumentation-to-evaluate-postural-balance-and-stability-on-an-unstable-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">572</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">64</span> Data Calibration of the Actual versus the Theoretical Micro Electro Mechanical Systems (MEMS) Based Accelerometer Reading through Remote Monitoring of Padre Jacinto Zamora Flyover</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Mark%20Payawal">John Mark Payawal</a>, <a href="https://publications.waset.org/abstracts/search?q=Francis%20Aldrine%20Uy"> Francis Aldrine Uy</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Paul%20Carreon"> John Paul Carreon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper shows the application of Structural Health Monitoring, SHM into bridges. Bridges are structures built to provide passage over a physical obstruction such as rivers, chasms or roads. The Philippines has a total of 8,166 national bridges as published on the 2015 atlas of the Department of Public Works and Highways (DPWH) and only 2,924 or 35.81% of these bridges are in good condition. As a result, PHP 30.464 billion of the 2016 budget of DPWH is allocated on roads and/or bridges maintenance alone. Intensive spending is owed to the present practice of outdated manual inspection and assessment, and poor structural health monitoring of Philippine infrastructures. As the School of Civil, Environmental, & Geological Engineering of Mapua Institute of Technology (MIT) continuous its well driven passion in research based projects, a partnership with the Department of Science and Technology (DOST) and the DPWH launched the application of Structural Health Monitoring, (SHM) in Padre Jacinto Zamora Flyover. The flyover is located along Nagtahan Boulevard in Sta. Mesa, Manila that connects Brgy. 411 and Brgy. 635. It gives service to vehicles going from Lacson Avenue to Mabini Bridge passing over Legarda Flyover. The flyover is chosen among the many located bridges in Metro Manila as the focus of the pilot testing due to its site accessibility, and complete structural built plans and specifications necessary for SHM as provided by the Bureau of Design, BOD department of DPWH. This paper focuses on providing a method to calibrate theoretical readings from STAAD Vi8 Pro and sync the data to actual MEMS accelerometer readings. It is observed that while the design standards used in constructing the flyover was reflected on the model, actual readings of MEMS accelerometer display a large difference compared to the theoretical data ran and taken from STAAD Vi8 Pro. In achieving a true seismic response of the modeled bridge or hence syncing the theoretical data to the actual sensor reading also called as the independent variable of this paper, analysis using single degree of freedom (SDOF) of the flyover under free vibration without damping using STAAD Vi8 Pro is done. The earthquake excitation and bridge responses are subjected to earthquake ground motion in the form of ground acceleration or Peak Ground Acceleration, PGA. Translational acceleration load is used to simulate the ground motion of the time history analysis acceleration record in STAAD Vi8 Pro. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20using%20single%20degree%20of%20freedom" title=" analysis using single degree of freedom"> analysis using single degree of freedom</a>, <a href="https://publications.waset.org/abstracts/search?q=micro%20electro%20mechanical%20system" title=" micro electro mechanical system"> micro electro mechanical system</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20ground%20acceleration" title=" peak ground acceleration"> peak ground acceleration</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a> </p> <a href="https://publications.waset.org/abstracts/74658/data-calibration-of-the-actual-versus-the-theoretical-micro-electro-mechanical-systems-mems-based-accelerometer-reading-through-remote-monitoring-of-padre-jacinto-zamora-flyover" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74658.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</span> 1-D Convolutional Neural Network Approach for Wheel Flat Detection for Freight Wagons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dachuan%20Shi">Dachuan Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hecht"> M. Hecht</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Ye"> Y. Ye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wheel%20flat" title=" wheel flat"> wheel flat</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/102932/1-d-convolutional-neural-network-approach-for-wheel-flat-detection-for-freight-wagons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">62</span> Decoding Kinematic Characteristics of Finger Movement from Electrocorticography Using Classical Methods and Deep Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ksenia%20Volkova">Ksenia Volkova</a>, <a href="https://publications.waset.org/abstracts/search?q=Artur%20Petrosyan"> Artur Petrosyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ignatii%20Dubyshkin"> Ignatii Dubyshkin</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexei%20Ossadtchi"> Alexei Ossadtchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ECoG" title=" ECoG"> ECoG</a>, <a href="https://publications.waset.org/abstracts/search?q=movement%20decoding" title=" movement decoding"> movement decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=sensorimotor%20cortex" title=" sensorimotor cortex"> sensorimotor cortex</a> </p> <a href="https://publications.waset.org/abstracts/88212/decoding-kinematic-characteristics-of-finger-movement-from-electrocorticography-using-classical-methods-and-deep-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">61</span> Condition Monitoring of a 3-Ø Induction Motor by Vibration Spectrum Analysis Using FFT Analyzer- a Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adi%20Narayana%20S%20Sudhakar.%20I">Adi Narayana S Sudhakar. I</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy conversion is one of the inevitable parts of any industries. It involves either conversion of mechanical energy in to electrical or vice versa. The later conversion of energy i.e. electrical to mechanical emphasizes the need of motor .Statistics reveals, about 8 % of industries’ annual turnover met on maintenance. Thus substantial numbers of efforts are required to minimize in incurring expenditure met towards break down maintenance. Condition monitoring is one of such techniques based on vibration widely used to recognize premature failures and paves a way to minimize cumbersome involved during breakdown of machinery. The present investigation involves a case study of squirrel cage induction motor (frequently in the electro machines) has been chosen for the conditional monitoring to predict its soundness on the basis of results of FFT analyser. Accelerometer which measures the acceleration converts in to impulses by FFT analyser generates vibration spectrum and time spectrum has been located at various positions on motor under different conditions. Results obtained from the FFT analyzer are compared to that of ISO standard vibration severity charts are taken to predict the preventative condition of considered machinery. Initial inspection of motor revealed that stator faults, broken end rings in rotor, eccentricity faults and misalignment between bearings are trouble shootings areas for present investigation. From the results of the shaft frequencies, it can be perceived that there is a misalignment between the bearings at both the ends. The higher order harmonics of FTF shows the presence of cracks on the race of the bearings at both the ends which are in the incipient stage. Replacement of the bearings at both the drive end (6306) and non-drive end (6206) and the alignment check between the bearings in the shaft are suggested as the constructive measures towards preventive maintenance of considered squirrel cage induction motor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FFT%20analyser" title="FFT analyser">FFT analyser</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20spectrum" title=" vibration spectrum"> vibration spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20spectrum%20accelerometer" title=" time spectrum accelerometer"> time spectrum accelerometer</a> </p> <a href="https://publications.waset.org/abstracts/19331/condition-monitoring-of-a-3-o-induction-motor-by-vibration-spectrum-analysis-using-fft-analyzer-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">60</span> Description of the Process Which Determine the Criterion Validity of Semi-Structured Interview PARA-SCI.CZ</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jarmila%20%C5%A0t%C4%9Bp%C3%A1nov%C3%A1">Jarmila Štěpánová</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Kudl%C3%A1%C4%8Dek"> Martin Kudláček</a>, <a href="https://publications.waset.org/abstracts/search?q=Luk%C3%A1%C5%A1%20Jakubec"> Lukáš Jakubec</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The people with spinal cord injury are one of the least sport active members of our society. Their hypoactivity is determined by primary injury, i.e., the loss of motor function, the injured part of the body is connected with health complications and social handicap. Study performs one part of the standardization process of semi-structured interview PARA-SCI.CZ (Czech version of the Physical Activity Recall Assessment for People with Spinal Cord Injury), which measures the type, frequency, duration, and intensity of physical activity of people with spinal cord injury. The study focused on persons with paraplegia who use a wheelchair as their primary mode of mobility. The aim of this study was to perform a process to determine the criterion validity of PARA-SCI.CZ. The actual physical activity of wheelchair users was monitored during three days by using accelerometers Actigraph GT3X fixed on the non-dominant wrist, and semi-structured interview PARA-SCI.CZ. During the PARA-SCI.CZ interview, participants were asked to recall activities they had done over the past 3 days, starting with the previous day. PARA-SCI.CZ captured frequency, duration, and intensity (low, moderate, and heavy) of two categories of physical activity (leisure time physical activity and activities of a usual day). Accelerometer Actigraph GT3X captured duration and intensity (low and moderate + heavy) of physical activity during three days and nights. The study presented three potential recalculations of measured data. Standardization process of PARA-SCI.CZ is essential to critically approach issues of health and active lifestyle of persons with spinal cord injury in the Czech Republic. Standardized PARA-SCI.CZ can be used in practice by physiotherapists and sports pedagogues from the field of adapted physical activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physical%20activity" title="physical activity">physical activity</a>, <a href="https://publications.waset.org/abstracts/search?q=lifestyle" title=" lifestyle"> lifestyle</a>, <a href="https://publications.waset.org/abstracts/search?q=paraplegia" title=" paraplegia"> paraplegia</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-structure%20interview" title=" semi-structure interview"> semi-structure interview</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a> </p> <a href="https://publications.waset.org/abstracts/67412/description-of-the-process-which-determine-the-criterion-validity-of-semi-structured-interview-para-scicz" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67412.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Seismic Response of Structure Using a Three Degree of Freedom Shake Table</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ketan%20N.%20Bajad">Ketan N. Bajad</a>, <a href="https://publications.waset.org/abstracts/search?q=Manisha%20V.%20Waghmare"> Manisha V. Waghmare</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquakes are the biggest threat to the civil engineering structures as every year it cost billions of dollars and thousands of deaths, around the world. There are various experimental techniques such as pseudo-dynamic tests – nonlinear structural dynamic technique, real time pseudo dynamic test and shaking table test method that can be employed to verify the seismic performance of structures. Shake table is a device that is used for shaking structural models or building components which are mounted on it. It is a device that simulates a seismic event using existing seismic data and nearly truly reproducing earthquake inputs. This paper deals with the use of shaking table test method to check the response of structure subjected to earthquake. The various types of shake table are vertical shake table, horizontal shake table, servo hydraulic shake table and servo electric shake table. The goal of this experiment is to perform seismic analysis of a civil engineering structure with the help of 3 degree of freedom (i.e. in X Y Z direction) shake table. Three (3) DOF shaking table is a useful experimental apparatus as it imitates a real time desired acceleration vibration signal for evaluating and assessing the seismic performance of structure. This study proceeds with the proper designing and erection of 3 DOF shake table by trial and error method. The table is designed to have a capacity up to 981 Newton. Further, to study the seismic response of a steel industrial building, a proportionately scaled down model is fabricated and tested on the shake table. The accelerometer is mounted on the model, which is used for recording the data. The experimental results obtained are further validated with the results obtained from software. It is found that model can be used to determine how the structure behaves in response to an applied earthquake motion, but the model cannot be used for direct numerical conclusions (such as of stiffness, deflection, etc.) as many uncertainties involved while scaling a small-scale model. The model shows modal forms and gives the rough deflection values. The experimental results demonstrate shake table as the most effective and the best of all methods available for seismic assessment of structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title="accelerometer">accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20degree%20of%20freedom%20shake%20table" title=" three degree of freedom shake table"> three degree of freedom shake table</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20analysis" title=" seismic analysis"> seismic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20industrial%20shed" title=" steel industrial shed"> steel industrial shed</a> </p> <a href="https://publications.waset.org/abstracts/98669/seismic-response-of-structure-using-a-three-degree-of-freedom-shake-table" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98669.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accelerometer&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accelerometer&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accelerometer&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div 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