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An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors.

Anwary, A.R., Yu, H. and Vassallo, M., 2018. An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors. Sensors, 18 (2), 676.

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sensors-18-00676-v2.pdf - Published Version
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DOI: 10.3390/s18020676


This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.

Item Type:Article
Additional Information:This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring.
Uncontrolled Keywords:accelerometer; asymmetry; feature extraction; gait analysis; gyroscope; inertial measurement unit; wearable sensors
Group:Faculty of Science & Technology
ID Code:30473
Deposited By: Symplectic RT2
Deposited On:13 Mar 2018 09:29
Last Modified:14 Mar 2022 14:10


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