Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Development and Assessment of Artificial Intelligence-Empowered Gait Monitoring System Using Single Inertial Sensor
AU - Zhou, Jie
AU - Mao, Qian
AU - Yang, Fan
AU - Zhang, Jun
AU - Shi, Menghan
AU - Hu, Zilin
A2 - Saggio, Giovanni
PY - 2024/9/16
Y1 - 2024/9/16
N2 - Gait instability is critical in medicine and healthcare, as it has associations with balance disorder and physical impairment. With the development of sensor technology, despite the fact that numerous wearable gait detection and recognition systems have been designed to monitor users’ gait patterns, they commonly spend a lot of time and effort to extract gait metrics from signal data. This study aims to design an artificial intelligence-empowered and economic-friendly gait monitoring system. A pair of intelligent shoes with a single inertial sensor and a smartphone application were developed as a gait monitoring system to detect users’ gait cycle, stand phase time, swing phase time, stride length, and foot clearance. We recruited 30 participants (24.09 ± 1.89 years) to collect gait data and used the Vicon motion capture system to verify the accuracy of the gait metrics. The results show that the gait monitoring system performs better on the assessment of the gait metrics. The accuracy of stride length and foot clearance is 96.17% and 92.07%, respectively. The artificial intelligence-empowered gait monitoring system holds promising potential for improving gait analysis and monitoring in the medical and healthcare fields.
AB - Gait instability is critical in medicine and healthcare, as it has associations with balance disorder and physical impairment. With the development of sensor technology, despite the fact that numerous wearable gait detection and recognition systems have been designed to monitor users’ gait patterns, they commonly spend a lot of time and effort to extract gait metrics from signal data. This study aims to design an artificial intelligence-empowered and economic-friendly gait monitoring system. A pair of intelligent shoes with a single inertial sensor and a smartphone application were developed as a gait monitoring system to detect users’ gait cycle, stand phase time, swing phase time, stride length, and foot clearance. We recruited 30 participants (24.09 ± 1.89 years) to collect gait data and used the Vicon motion capture system to verify the accuracy of the gait metrics. The results show that the gait monitoring system performs better on the assessment of the gait metrics. The accuracy of stride length and foot clearance is 96.17% and 92.07%, respectively. The artificial intelligence-empowered gait monitoring system holds promising potential for improving gait analysis and monitoring in the medical and healthcare fields.
KW - healthcare
KW - wearable system
KW - gait monitoring
KW - sensor
KW - artificial intelligence algorithm
U2 - 10.3390/s24185998
DO - 10.3390/s24185998
M3 - Journal article
VL - 24
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 18
M1 - 5998
ER -