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Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology

Cao, Huiyi
http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549

Abstract Details

2020, Master of Sciences, Case Western Reserve University, EECS - Computer and Information Sciences.
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.
Ming-Chun Huang (Committee Chair)
An Wang (Committee Member)
Shuai Xu (Committee Member)
Computer Science
Remote Gait Monitoring; Gait Parameters; Distributed Deep Learning Optimized System

Recommended Citations

Citations

  • Cao, H. (2020). Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549

    APA Style (7th edition)

  • Cao, Huiyi. Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology. 2020. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549.

    MLA Style (8th edition)

  • Cao, Huiyi. "Remote Gait Monitoring Mobile System Enabled by Wearable Sensor Technology." Master's thesis, Case Western Reserve University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1587042096284549

    Chicago Manual of Style (17th edition)

case1587042096284549
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© 2020, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.