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KMID : 1142420200180020287
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PNF and Movement
2020 Volume.18 No. 2 p.287 ~ p.296
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Development of a Wearable Inertial Sensor-based Gait Analysis Device Using Machine Learning Algorithms -Validity of the Temporal Gait Parameter in Healthy Young Adults-
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Seol Pyong-Wha
Yoo Heung-Jong
Choi Yoon-Chul
Shin Min-Yong
Choo Kwang-Jae
Kim Kyoung-Shin
Baek Seung-Yoon
Lee Yong-Woo
Song Chang-Ho
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Abstract
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Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters.
Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system.
Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99∼0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31∼1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98∼0.99), and CV error values were very small for all gait parameters (0.33∼1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86∼0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17∼5.58%).
Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.
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KEYWORD
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Gait, Machine learning, Wearable electronic devices, Motion
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