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- 저자 : 설평화 (검색결과 1 건)
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설평화,유흥종,최윤철,신민용,추광재,김경신,백승윤,이용우,송창호 대한고유수용성신경근촉진법학회 2020 PNF and Movement Vol.18 No.2
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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