Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models

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<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> 88649</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milagros%20Ja%C3%A9n-Vargas">Milagros Ja茅n-Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Garc%C3%ADa%20Mart%C3%ADnez"> Javier Garc铆a Mart铆nez</a>, <a href="https://publications.waset.org/abstracts/search?q=Karla%20Miriam%20Reyes%20Leiva"> Karla Miriam Reyes Leiva</a>, <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20Fernanda%20Trujillo-Guerrero"> Mar铆a Fernanda Trujillo-Guerrero</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20Fernandes"> Francisco Fernandes</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C3%A9rgio%20Barroso%20Gon%C3%A7alves"> S茅rgio Barroso Gon莽alves</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Tavares%20Silva"> Miguel Tavares Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Sim%C3%B5es%20Lopes"> Daniel Sim玫es Lopes</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Javier%20Serrano%20Olmedo"> Jos茅 Javier Serrano Olmedo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals. <iframe src="https://publications.waset.org/abstracts/167055.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HAR" title="HAR">HAR</a>, <a href="https://publications.waset.org/abstracts/search?q=IMU" title=" IMU"> IMU</a>, <a href="https://publications.waset.org/abstracts/search?q=MOCAP" title=" MOCAP"> MOCAP</a>, <a href="https://publications.waset.org/abstracts/search?q=acceleration" title=" acceleration"> acceleration</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity" title=" velocity"> velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=position" title=" position"> position</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20maps" title=" feature maps"> feature maps</a> </p> <a href="https://publications.waset.org/abstracts/167055/impact-of-integrated-signals-for-doing-human-activity-recognition-using-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167055.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">106</span> </span> </div> </div> </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 class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models