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Title: MGait : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors
Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.  more » « less
Award ID(s):
2114499
PAR ID:
10334210
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ACM Transactions on Internet of Things
Volume:
3
Issue:
1
ISSN:
2691-1914
Page Range / eLocation ID:
1 to 24
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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