Low-Cost Multisensor Integrated System for Online Walking Gait Detection

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that...

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Bibliographic Details
Published in:Journal of sensors 2021, Vol.2021 (1)
Main Authors: Yan, Lingyun, Wei, Guowu, Hu, Zheqi, Xiu, Haohua, Wei, Yuyang, Ren, Lei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biomechanics
Calibration
Controllers
Data collection
Design
Gait
Gait recognition
Heels
Kinematics
Low cost
Motion capture
Multilayer perceptrons
Neural networks
Personal computers
Real time
Sensors
Three dimensional motion
Treadmills
Walking
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Summary:A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.
ISSN:1687-725X
1687-7268
DOI:10.1155/2021/6378514