TY - GEN
T1 - Short Stick Exercise Tracking System for Elderly Rehabilitation using IMU Sensor
AU - Oi, Kazuki
AU - Nakamura, Yugo
AU - Matsuda, Yuki
AU - Fujimoto, Manato
AU - Yasumoto, Keiichi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Stick exercises, which have been attracting attention for improving the health of the elderly, are usually performed in nursing homes under the guidance of nursing staff. However, in the current pandemic in which the elderly are advised to refrain from going out unnecessarily, it is desirable for each individual to be able to perform the stick exercises alone. In this study, we aim to develop a stick exercise support system that can automatically record the number of times an elderly person performs each type of stick exercise and provide feedback to improve the movement for each exercise. As a first step toward the realization of this stick exercise support system, we investigated a method for recognizing exercise movements using inertial measurement unit (IMU) sensors. In the evaluation experiment, 21 subjects performed 3 sets (10 times per set) of eight basic stick exercises. The exercise movements were classified based on the linear acceleration and quaternion data obtained from the IMU. As a result, 90% of F-measure was achieved when using Light GBM as the learning algorithm.
AB - Stick exercises, which have been attracting attention for improving the health of the elderly, are usually performed in nursing homes under the guidance of nursing staff. However, in the current pandemic in which the elderly are advised to refrain from going out unnecessarily, it is desirable for each individual to be able to perform the stick exercises alone. In this study, we aim to develop a stick exercise support system that can automatically record the number of times an elderly person performs each type of stick exercise and provide feedback to improve the movement for each exercise. As a first step toward the realization of this stick exercise support system, we investigated a method for recognizing exercise movements using inertial measurement unit (IMU) sensors. In the evaluation experiment, 21 subjects performed 3 sets (10 times per set) of eight basic stick exercises. The exercise movements were classified based on the linear acceleration and quaternion data obtained from the IMU. As a result, 90% of F-measure was achieved when using Light GBM as the learning algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85134299365&partnerID=8YFLogxK
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U2 - 10.1109/CPHS56133.2022.9804564
DO - 10.1109/CPHS56133.2022.9804564
M3 - Conference contribution
AN - SCOPUS:85134299365
T3 - Proceedings - 2nd International Workshop on Cyber-Physical-Human System Design and Implementation, CPHS 2022
SP - 13
EP - 18
BT - Proceedings - 2nd International Workshop on Cyber-Physical-Human System Design and Implementation, CPHS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Workshop on Cyber-Physical-Human System Design and Implementation, CPHS 2022
Y2 - 3 May 2022
ER -