Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology

Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology

Bibliographic Details
Main Author: Cao, Huiyi
Language:English
Published: Case Western Reserve University School of Graduate Studies / OhioLINK 2020
Subjects:
Computer Science
Remote Gait Monitoring
Gait Parameters
Distributed Deep Learning Optimized System
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-case15870420962845492021-08-03T07:14:29Z Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology Cao, Huiyi Computer Science Remote Gait Monitoring Gait Parameters Distributed Deep Learning Optimized System Remote gait monitoring system plays an important role in improving the process of gait rehabilitation while patients are not supervised by the physical therapists outside of the clinics. It can benefit patients, providers, and payers with low cost, high accuracy, real-time accessibility, detailed exercise reports, preventive treatment, and data privacy. The patients can use the system to record all the exercise during the recovery process while the providers can access the patients' recovery process with more convenience remotely. The remote gait monitoring system can also improve providers like insurance companies to create a more customized health plan and establish quantitative regulation. In this study, three gait parameters are discussed, including stride length, stride frequency, and stride velocity. A classification model was used to detect stationary epoch and non-stationary epoch to extract each stride sample. The accuracy of the classification model achieves 99.3%, which shows high reliability for detecting motion change points. Then, a mobile application on stride length estimation was developed with an OpenMP-based distributed deep learning optimized system (DDOS). The DDOS system used a convolutional neural network (CNN) to estimate the stride length of each stride sample. The system has the advantages of incremental learning, time flexibility, and customization, which can be used for multiple users at any place during the same time. The experiment results show a high accuracy with stride length estimation. OpenMP was used to accelerate operation time since the training process of CNN is time-consuming. 2020-05-29 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549 http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Science
Remote Gait Monitoring
Gait Parameters
Distributed Deep Learning Optimized System
spellingShingle Computer Science
Remote Gait Monitoring
Gait Parameters
Distributed Deep Learning Optimized System
Cao, Huiyi
Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
author Cao, Huiyi
author_facet Cao, Huiyi
author_sort Cao, Huiyi
title Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
title_short Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
title_full Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
title_fullStr Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
title_full_unstemmed Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology
title_sort remote gait monitoring mobile system enabled by wearable sensor technology
publisher Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate 2020
url http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549
work_keys_str_mv AT caohuiyi remotegaitmonitoringmobilesystemenabledbywearablesensortechnology
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