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recognition</title> <meta name="description" content="Search results for: human motion recognition"> <meta name="keywords" content="human motion recognition"> <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 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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="human motion recognition"> <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> 10836</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: human motion recognition</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10836</span> Relevant LMA Features for Human Motion Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Ajili">Insaf Ajili</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Mallem"> Malik Mallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Didier"> Jean-Yves Didier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discriminative%20LMA%20features" title="discriminative LMA features">discriminative LMA features</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20reduction" title=" features reduction"> features reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition" title=" human motion recognition"> human motion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/96299/relevant-lma-features-for-human-motion-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96299.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">195</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">10835</span> Specified Human Motion Recognition and Unknown Hand-Held Object Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinsiang%20Shaw">Jinsiang Shaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Pik-Hoe%20Chen"> Pik-Hoe Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Automatic%20Tracking" title="Automatic Tracking">Automatic Tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=Back%20Projection" title=" Back Projection"> Back Projection</a>, <a href="https://publications.waset.org/abstracts/search?q=Motion%20Recognition" title=" Motion Recognition"> Motion Recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoplifting" title=" Shoplifting"> Shoplifting</a> </p> <a href="https://publications.waset.org/abstracts/66866/specified-human-motion-recognition-and-unknown-hand-held-object-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66866.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">10834</span> Human Action Recognition Using Wavelets of Derived Beta Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neziha%20Jaouedi">Neziha Jaouedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Boujnah"> Noureddine Boujnah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Salim%20Bouhlel"> Mohamed Salim Bouhlel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feautures%20extraction" title="feautures extraction">feautures extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20action%20classifier" title=" human action classifier"> human action classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20neural%20network" title=" wavelet neural network"> wavelet neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=beta%20wavelet" title=" beta wavelet"> beta wavelet</a> </p> <a href="https://publications.waset.org/abstracts/79396/human-action-recognition-using-wavelets-of-derived-beta-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79396.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">411</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">10833</span> Human Motion Capture: New Innovations in the Field of Computer Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najm%20Alotaibi">Najm Alotaibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human motion capture has become one of the major area of interest in the field of computer vision. Some of the major application areas that have been rapidly evolving include the advanced human interfaces, virtual reality and security/surveillance systems. This study provides a brief overview of the techniques and applications used for the markerless human motion capture, which deals with analyzing the human motion in the form of mathematical formulations. The major contribution of this research is that it classifies the computer vision based techniques of human motion capture based on the taxonomy, and then breaks its down into four systematically different categories of tracking, initialization, pose estimation and recognition. The detailed descriptions and the relationships descriptions are given for the techniques of tracking and pose estimation. The subcategories of each process are further described. Various hypotheses have been used by the researchers in this domain are surveyed and the evolution of these techniques have been explained. It has been concluded in the survey that most researchers have focused on using the mathematical body models for the markerless motion capture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20capture" title="human motion capture">human motion capture</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=vision-based" title=" vision-based"> vision-based</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/22770/human-motion-capture-new-innovations-in-the-field-of-computer-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22770.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">319</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">10832</span> An Efficient Motion Recognition System Based on LMA Technique and a Discrete Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Ajili">Insaf Ajili</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Mallem"> Malik Mallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Didier"> Jean-Yves Didier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition" title="human motion recognition">human motion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20representation" title=" motion representation"> motion representation</a>, <a href="https://publications.waset.org/abstracts/search?q=Laban%20Movement%20Analysis" title=" Laban Movement Analysis"> Laban Movement Analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Hidden%20Markov%20Model" title=" Discrete Hidden Markov Model"> Discrete Hidden Markov Model</a> </p> <a href="https://publications.waset.org/abstracts/87469/an-efficient-motion-recognition-system-based-on-lma-technique-and-a-discrete-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87469.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">207</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">10831</span> A Motion Dictionary to Real-Time Recognition of Sign Language Alphabet Using Dynamic Time Warping and Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcio%20Leal">Marcio Leal</a>, <a href="https://publications.waset.org/abstracts/search?q=Marta%20Villamil"> Marta Villamil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping" title=" dynamic time warping"> dynamic time warping</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared" title=" infrared"> infrared</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language%20recognition" title=" sign language recognition"> sign language recognition</a> </p> <a href="https://publications.waset.org/abstracts/94322/a-motion-dictionary-to-real-time-recognition-of-sign-language-alphabet-using-dynamic-time-warping-and-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94322.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">216</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">10830</span> Cepstrum Analysis of Human Walking Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Koichi%20Kurita">Koichi Kurita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose a real-time data collection technique for the detection of human walking motion from the charge generated on the human body. This technique is based on the detection of a sub-picoampere electrostatic induction current, generated by the motion, flowing through the electrode of a wireless portable sensor attached to the subject. An FFT analysis of the wave-forms of the electrostatic induction currents generated by the walking motions showed that the currents generated under normal and restricted walking conditions were different. Moreover, we carried out a cepstrum analysis to detect any differences in the walking style. Results suggest that a slight difference in motion, either due to the individual’s gait or a splinted leg, is directly reflected in the electrostatic induction current generated by the walking motion. The proposed wireless portable sensor enables the detection of even subtle differences in walking motion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20walking%20motion" title="human walking motion">human walking motion</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20measurement" title=" motion measurement"> motion measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20measurement" title=" current measurement"> current measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=electrostatic%20induction" title=" electrostatic induction"> electrostatic induction</a> </p> <a href="https://publications.waset.org/abstracts/12335/cepstrum-analysis-of-human-walking-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12335.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">344</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">10829</span> 3D Human Reconstruction over Cloud Based Image Data via AI and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaushik%20Sathupadi">Kaushik Sathupadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandesh%20Achar"> Sandesh Achar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20analysis" title=" human motion analysis"> human motion analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</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/190028/3d-human-reconstruction-over-cloud-based-image-data-via-ai-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190028.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">36</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">10828</span> A Contribution to Human Activities Recognition Using Expert System Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/171721/a-contribution-to-human-activities-recognition-using-expert-system-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171721.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">118</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">10827</span> Human Activities Recognition Based on Expert System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/151943/human-activities-recognition-based-on-expert-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151943.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">139</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">10826</span> Interactive Shadow Play Animation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wan">Bo Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiu%20Wen"> Xiu Wen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingling%20An"> Lingling An</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoling%20Ding"> Xiaoling Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes a Chinese shadow play animation system based on Kinect. Users, without any professional training, can personally manipulate the shadow characters to finish a shadow play performance by their body actions and get a shadow play video through giving the record command to our system if they want. In our system, Kinect is responsible for capturing human movement and voice commands data. Gesture recognition module is used to control the change of the shadow play scenes. After packaging the data from Kinect and the recognition result from gesture recognition module, VRPN transmits them to the server-side. At last, the server-side uses the information to control the motion of shadow characters and video recording. This system not only achieves human-computer interaction, but also realizes the interaction between people. It brings an entertaining experience to users and easy to operate for all ages. Even more important is that the application background of Chinese shadow play embodies the protection of the art of shadow play animation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hadow%20play%20animation" title="hadow play animation">hadow play animation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kinect" title=" Kinect"> Kinect</a>, <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title=" gesture recognition"> gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=VRPN" title=" VRPN"> VRPN</a>, <a href="https://publications.waset.org/abstracts/search?q=HCI" title=" HCI"> HCI</a> </p> <a href="https://publications.waset.org/abstracts/19293/interactive-shadow-play-animation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19293.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">401</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">10825</span> Vision-Based Hand Segmentation Techniques for Human-Computer Interaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Jebali">M. Jebali</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Jemni"> M. Jemni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm such as an input to another system which attempt to bring the HCI performance nearby the human-human interaction, by modeling an intelligent sign language recognition system based on prediction in the context of dialogue between the system (avatar) and the interlocutor. For the purpose of hand segmentation, an overcoming occlusion approach has been proposed for superior results for detection of hand from an image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HCI" title="HCI">HCI</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language%20recognition" title=" sign language recognition"> sign language recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20segmentation" title=" hand segmentation"> hand segmentation</a> </p> <a href="https://publications.waset.org/abstracts/26490/vision-based-hand-segmentation-techniques-for-human-computer-interaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26490.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">412</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">10824</span> Fitness Action Recognition Based on MediaPipe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zixuan%20Xu">Zixuan Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yichun%20Lou"> Yichun Lou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Song"> Yang Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihuai%20Lin"> Zihuai Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=MediaPipe" title=" MediaPipe"> MediaPipe</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20dynamic%20time%20warping" title=" fast dynamic time warping"> fast dynamic time warping</a> </p> <a href="https://publications.waset.org/abstracts/160758/fitness-action-recognition-based-on-mediapipe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160758.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">118</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">10823</span> Advanced Mouse Cursor Control and Speech Recognition Module</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prasad%20Kalagura">Prasad Kalagura</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Veeresh%20kumar"> B. Veeresh kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=embedded%20ARM7%20processor" title="embedded ARM7 processor">embedded ARM7 processor</a>, <a href="https://publications.waset.org/abstracts/search?q=mouse%20pointer%20control" title=" mouse pointer control"> mouse pointer control</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20recognition" title=" voice recognition "> voice recognition </a> </p> <a href="https://publications.waset.org/abstracts/31757/advanced-mouse-cursor-control-and-speech-recognition-module" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31757.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">578</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">10822</span> Intelligent Human Pose Recognition Based on EMG Signal Analysis and Machine 3D Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Si%20Chen">Si Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Quanhong%20Jiang"> Quanhong Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the increasingly mature posture recognition technology, human movement information is widely used in sports rehabilitation, human-computer interaction, medical health, human posture assessment, and other fields today; this project uses the most original ideas; it is proposed to use the collection equipment for the collection of myoelectric data, reflect the muscle posture change on a degree of freedom through data processing, carry out data-muscle three-dimensional model joint adjustment, and realize basic pose recognition. Based on this, bionic aids or medical rehabilitation equipment can be further developed with the help of robotic arms and cutting-edge technology, which has a bright future and unlimited development space. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pose%20recognition" title="pose recognition">pose recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20animation" title=" 3D animation"> 3D animation</a>, <a href="https://publications.waset.org/abstracts/search?q=electromyography" title=" electromyography"> electromyography</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=bionics" title=" bionics"> bionics</a> </p> <a href="https://publications.waset.org/abstracts/166827/intelligent-human-pose-recognition-based-on-emg-signal-analysis-and-machine-3d-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166827.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">79</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">10821</span> Smartphone-Based Human Activity Recognition by Machine Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanting%20Cao">Yanting Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazumitsu%20Nawata"> Kazumitsu Nawata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20sensors" title="smart sensors">smart sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title=" human activity recognition"> human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/142359/smartphone-based-human-activity-recognition-by-machine-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142359.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">143</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">10820</span> An Agent-Based Modeling and Simulation of Human Muscle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sina%20Saadati">Sina Saadati</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Razzazi"> Mohammadreza Razzazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we have tried to present an agent-based model of human muscle. A suitable model of muscle is necessary for the analysis of mankind's movements. It can be used by clinical researchers who study the influence of motion sicknesses, like Parkinson's disease. It is also useful in the development of a prosthesis that receives the electromyography signals and generates force as a reaction. Since we have focused on computational efficiency in this research, the model can compute the calculations very fast. As far as it concerns prostheses, the model can be known as a charge-efficient method. In this paper, we are about to illustrate an agent-based model. Then, we will use it to simulate the human gait cycle. This method can also be done reversely in the analysis of gait in motion sicknesses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agent-based%20modeling%20and%20simulation" title="agent-based modeling and simulation">agent-based modeling and simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20muscle" title=" human muscle"> human muscle</a>, <a href="https://publications.waset.org/abstracts/search?q=gait%20cycle" title=" gait cycle"> gait cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20sickness" title=" motion sickness"> motion sickness</a> </p> <a href="https://publications.waset.org/abstracts/149021/an-agent-based-modeling-and-simulation-of-human-muscle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149021.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">114</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">10819</span> An Assistive Robotic Arm for Defence and Rescue Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Harrison%20Kurunathan">J. Harrison Kurunathan</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Jayaparvathy"> R. Jayaparvathy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> "Assistive Robotics" is the field that deals with the study of robots that helps in human motion and also empowers human abilities by interfacing the robotic systems to be manipulated by human motion. The proposed model is a robotic arm that works as a haptic interface on the basis on accelerometers and DC motors that will function with respect to the movement of the human muscle. The proposed model would effectively work as a haptic interface that would reduce human effort in the field of defense and rescue. This can be used in very critical conditions like fire accidents to avoid causalities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerometers" title="accelerometers">accelerometers</a>, <a href="https://publications.waset.org/abstracts/search?q=haptic%20interface" title=" haptic interface"> haptic interface</a>, <a href="https://publications.waset.org/abstracts/search?q=servo%20motors" title=" servo motors"> servo motors</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/6771/an-assistive-robotic-arm-for-defence-and-rescue-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6771.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">397</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">10818</span> Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Hsiang%20Chang">Yuan-Hsiang Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pin-Chi%20Lin"> Pin-Chi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Der%20Jeng"> Li-Der Jeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20detection" title="motion detection">motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20tracking" title=" motion tracking"> motion tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20analysis" title=" trajectory analysis"> trajectory analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/13650/automatic-motion-trajectory-analysis-for-dual-human-interaction-using-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13650.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">548</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">10817</span> Automatic Speech Recognition Systems Performance Evaluation Using Word Error Rate Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jo%C3%A3o%20Rato">João Rato</a>, <a href="https://publications.waset.org/abstracts/search?q=Nuno%20Costa"> Nuno Costa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The human verbal communication is a two-way process which requires a mutual understanding that will result in some considerations. This kind of communication, also called dialogue, besides the supposed human agents it can also be performed between human agents and machines. The interaction between Men and Machines, by means of a natural language, has an important role concerning the improvement of the communication between each other. Aiming at knowing the performance of some speech recognition systems, this document shows the results of the accomplished tests according to the Word Error Rate evaluation method. Besides that, it is also given a set of information linked to the systems of Man-Machine communication. After this work has been made, conclusions were drawn regarding the Speech Recognition Systems, among which it can be mentioned their poor performance concerning the voice interpretation in noisy environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=man-machine%20conversation" title=" man-machine conversation"> man-machine conversation</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=spoken%20dialogue%20systems" title=" spoken dialogue systems"> spoken dialogue systems</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20error%20rate" title=" word error rate"> word error rate</a> </p> <a href="https://publications.waset.org/abstracts/62274/automatic-speech-recognition-systems-performance-evaluation-using-word-error-rate-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62274.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">322</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">10816</span> Laban Movement Analysis Using Kinect</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernstein%20Ran">Bernstein Ran</a>, <a href="https://publications.waset.org/abstracts/search?q=Shafir%20Tal"> Shafir Tal</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsachor%20Rachelle"> Tsachor Rachelle</a>, <a href="https://publications.waset.org/abstracts/search?q=Studd%20Karen"> Studd Karen</a>, <a href="https://publications.waset.org/abstracts/search?q=Schuster%20Assaf"> Schuster Assaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Laban Movement Analysis (LMA), developed in the dance community over the past seventy years, is an effective method for observing, describing, notating, and interpreting human movement to enhance communication and expression in everyday and professional life. Many applications that use motion capture data might be significantly leveraged if the Laban qualities will be recognized automatically. This paper presents an automated recognition method of Laban qualities from motion capture skeletal recordings and it is demonstrated on the output of Microsoft’s Kinect V2 sensor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laban%20movement%20analysis" title="Laban movement analysis">Laban movement analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multitask%20learning" title=" multitask learning"> multitask learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Kinect%20sensor" title=" Kinect sensor"> Kinect sensor</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/25365/laban-movement-analysis-using-kinect" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25365.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">341</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">10815</span> Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gheida%20J.%20Shahrour">Gheida J. Shahrour</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20J.%20Russell"> Martin J. Russell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=person%20recognition" title="person recognition">person recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20recognition" title=" topic recognition"> topic recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=culture%20recognition" title=" culture recognition"> culture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20body%20movement%20signals" title=" 3D body movement signals"> 3D body movement signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20compensation" title=" variability compensation"> variability compensation</a> </p> <a href="https://publications.waset.org/abstracts/19473/recognizing-an-individual-their-topic-of-conversation-and-cultural-background-from-3d-body-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19473.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">541</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">10814</span> A Human Centered Design of an Exoskeleton Using Multibody Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sebastian%20K%C3%B6lbl">Sebastian Kölbl</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Reitmaier"> Thomas Reitmaier</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Hartmann"> Mathias Hartmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trial and error approaches to adapt wearable support structures to human physiology are time consuming and elaborate. However, during preliminary design, the focus lies on understanding the interaction between exoskeleton and the human body in terms of forces and moments, namely body mechanics. For the study at hand, a multi-body simulation approach has been enhanced to evaluate actual forces and moments in a human dummy model with and without a digital mock-up of an active exoskeleton. Therefore, different motion data have been gathered and processed to perform a musculosceletal analysis. The motion data are ground reaction forces, electromyography data (EMG) and human motion data recorded with a marker-based motion capture system. Based on the experimental data, the response of the human dummy model has been calibrated. Subsequently, the scalable human dummy model, in conjunction with the motion data, is connected with the exoskeleton structure. The results of the human-machine interaction (HMI) simulation platform are in particular resulting contact forces and human joint forces to compare with admissible values with regard to the human physiology. Furthermore, it provides feedback for the sizing of the exoskeleton structure in terms of resulting interface forces (stress justification) and the effect of its compliance. A stepwise approach for the setup and validation of the modeling strategy is presented and the potential for a more time and cost-effective development of wearable support structures is outlined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assistive%20devices" title="assistive devices">assistive devices</a>, <a href="https://publications.waset.org/abstracts/search?q=ergonomic%20design" title=" ergonomic design"> ergonomic design</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20dynamics" title=" inverse dynamics"> inverse dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20kinematics" title=" inverse kinematics"> inverse kinematics</a>, <a href="https://publications.waset.org/abstracts/search?q=multibody%20simulation" title=" multibody simulation"> multibody simulation</a> </p> <a href="https://publications.waset.org/abstracts/151467/a-human-centered-design-of-an-exoskeleton-using-multibody-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151467.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">162</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">10813</span> Efficient Human Motion Detection Feature Set by Using Local Phase Quantization Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arwa%20Alzughaibi">Arwa Alzughaibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20detection" title="human motion detection">human motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=histograms%20of%20oriented%20gradient" title=" histograms of oriented gradient"> histograms of oriented gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a> </p> <a href="https://publications.waset.org/abstracts/48160/efficient-human-motion-detection-feature-set-by-using-local-phase-quantization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48160.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">257</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">10812</span> Hand Detection and Recognition for Malay Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Noah%20A.%20Rahman">Mohd Noah A. Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Afzaal%20H.%20Seyal"> Afzaal H. Seyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Norhafilah%20Bara"> Norhafilah Bara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Developing a software application using an interface with computers and peripheral devices using gestures of human body such as hand movements keeps growing in interest. A review on this hand gesture detection and recognition based on computer vision technique remains a very challenging task. This is to provide more natural, innovative and sophisticated way of non-verbal communication, such as sign language, in human computer interaction. Nevertheless, this paper explores hand detection and hand gesture recognition applying a vision based approach. The hand detection and recognition used skin color spaces such as HSV and YCrCb are applied. However, there are limitations that are needed to be considered. Almost all of skin color space models are sensitive to quickly changing or mixed lighting circumstances. There are certain restrictions in order for the hand recognition to give better results such as the distance of user’s hand to the webcam and the posture and size of the hand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hand%20detection" title="hand detection">hand detection</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20gesture" title=" hand gesture"> hand gesture</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20recognition" title=" hand recognition"> hand recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language" title=" sign language"> sign language</a> </p> <a href="https://publications.waset.org/abstracts/46765/hand-detection-and-recognition-for-malay-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46765.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">306</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">10811</span> Highly Accurate Target Motion Compensation Using Entropy Function Minimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amin%20Aghatabar%20Roodbary">Amin Aghatabar Roodbary</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hassan%20Bastani"> Mohammad Hassan Bastani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the defects of stepped frequency radar systems is their sensitivity to target motion. In such systems, target motion causes range cell shift, false peaks, Signal to Noise Ratio (SNR) reduction and range profile spreading because of power spectrum interference of each range cell in adjacent range cells which induces distortion in High Resolution Range Profile (HRRP) and disrupt target recognition process. Thus Target Motion Parameters (TMPs) effects compensation should be employed. In this paper, such a method for estimating TMPs (velocity and acceleration) and consequently eliminating or suppressing the unwanted effects on HRRP based on entropy minimization has been proposed. This method is carried out in two major steps: in the first step, a discrete search method has been utilized over the whole acceleration-velocity lattice network, in a specific interval seeking to find a less-accurate minimum point of the entropy function. Then in the second step, a 1-D search over velocity is done in locus of the minimum for several constant acceleration lines, in order to enhance the accuracy of the minimum point found in the first step. The provided simulation results demonstrate the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20target%20recognition%20%28ATR%29" title="automatic target recognition (ATR)">automatic target recognition (ATR)</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20resolution%20range%20profile%20%28HRRP%29" title=" high resolution range profile (HRRP)"> high resolution range profile (HRRP)</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20compensation" title=" motion compensation"> motion compensation</a>, <a href="https://publications.waset.org/abstracts/search?q=stepped%20frequency%20waveform%20technique%20%28SFW%29" title=" stepped frequency waveform technique (SFW)"> stepped frequency waveform technique (SFW)</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20motion%20parameters%20%28TMPs%29" title=" target motion parameters (TMPs)"> target motion parameters (TMPs)</a> </p> <a href="https://publications.waset.org/abstracts/97540/highly-accurate-target-motion-compensation-using-entropy-function-minimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97540.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">152</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">10810</span> Freedom of Information and Freedom of Expression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amin%20Pashaye%20Amiri">Amin Pashaye Amiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Freedom of information, according to which the public has a right to have access to government-held information, is largely considered as a tool for improving transparency and accountability in governments, and as a requirement of self-governance and good governance. So far, more than ninety countries have recognized citizens’ right to have access to public information. This recognition often took place through the adoption of an act referred to as “freedom of information act”, “access to public records act”, and so on. A freedom of information act typically imposes a positive obligation on a government to initially and regularly release certain public information, and also obliges it to provide individuals with information they request. Such an act usually allows governmental bodies to withhold information only when it falls within a limited number of exemptions enumerated in the act such as exemptions for protecting privacy of individuals and protecting national security. Some steps have been taken at the national and international level towards the recognition of freedom of information as a human right. Freedom of information was recognized in a few countries as a part of freedom of expression, and therefore, as a human right. Freedom of information was also recognized by some international bodies as a human right. The Inter-American Court of Human Rights ruled in 2006 that Article 13 of the American Convention on Human Rights, which concerns the human right to freedom of expression, protects the right of all people to request access to government information. The European Court of Human Rights has recently taken a considerable step towards recognizing freedom of information as a human right. However, in spite of the measures that have been taken, public access to government information is not yet widely accepted as an international human right. The paper will consider the degree to which freedom of information has been recognized as a human right, and study the possibility of widespread recognition of such a human right in the future. It will also examine the possible benefits of such recognition for the development of the human right to free expression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=freedom%20of%20information" title="freedom of information">freedom of information</a>, <a href="https://publications.waset.org/abstracts/search?q=freedom%20of%20expression" title=" freedom of expression"> freedom of expression</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20rights" title=" human rights"> human rights</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20information" title=" government information"> government information</a> </p> <a href="https://publications.waset.org/abstracts/23647/freedom-of-information-and-freedom-of-expression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23647.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">547</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">10809</span> Hand Motion and Gesture Control of Laboratory Test Equipment Using the Leap Motion Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20A.%20Grout">Ian A. Grout</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the design and development of a system to provide hand motion and gesture control of laboratory test equipment is considered and discussed. The Leap Motion controller is used to provide an input to control a laboratory power supply as part of an electronic circuit experiment. By suitable hand motions and gestures, control of the power supply is provided remotely and without the need to physically touch the equipment used. As such, it provides an alternative manner in which to control electronic equipment via a PC and is considered here within the field of human computer interaction (HCI). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control" title="control">control</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20gesture" title=" hand gesture"> hand gesture</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20computer%20interaction" title=" human computer interaction"> human computer interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20equipment" title=" test equipment"> test equipment</a> </p> <a href="https://publications.waset.org/abstracts/72099/hand-motion-and-gesture-control-of-laboratory-test-equipment-using-the-leap-motion-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72099.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">315</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">10808</span> Real Time Multi Person Action Recognition Using Pose Estimates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aishrith%20Rao">Aishrith Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20estimates" title=" pose estimates"> pose estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/127872/real-time-multi-person-action-recognition-using-pose-estimates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127872.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">139</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">10807</span> Lightweight Hybrid Convolutional and Recurrent Neural Networks for Wearable Sensor Based Human Activity Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonia%20Perez-Gamboa">Sonia Perez-Gamboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingquan%20Sun"> Qingquan Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Zhang"> Yan Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption. <p class="card-text"><strong>Keywords:</strong> <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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title=" human activity recognition"> human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=inertial%20sensor" title=" inertial sensor"> inertial sensor</a> </p> <a href="https://publications.waset.org/abstracts/131782/lightweight-hybrid-convolutional-and-recurrent-neural-networks-for-wearable-sensor-based-human-activity-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131782.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">150</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=human%20motion%20recognition&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition&page=4">4</a></li> <li class="page-item"><a class="page-link" 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