TY - GEN
T1 - Utilizing Wearable GRF and EMG Sensing System and Machine Learning Algorithms to Enable Locomotion Mode Recognition for In-home Rehabilitation
AU - Fang, Chaoming
AU - Wang, Yixuan
AU - Gao, Shuo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8/16
Y1 - 2020/8/16
N2 - Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years, locomotion mode recognition using wearable sensors plays an important role in the field of in-home rehabilitation. In this paper, a smart sensing system utilizing flexible electromyography (EMG) sensors and ground reaction force (GRF) sensors for locomotion mode recognition is presented, together with its use under the IoHT architecture. EMG and GRF information from ten healthy subjects in five common locomotion modes in daily life were collected, analyzed, and then transmitted to remote end terminals (e.g., personal computers). The data analysis process was implemented with machine learning techniques (Support Vector Machine), through which the locomotion modes were determined with a high accuracy of 96.38%. This article demonstrates a feasible means for accurate locomotion mode recognition by combining wearable sensing techniques and the machine learning algorithm, potentially advancing the development for IoHT based in-home rehabilitation.
AB - Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years, locomotion mode recognition using wearable sensors plays an important role in the field of in-home rehabilitation. In this paper, a smart sensing system utilizing flexible electromyography (EMG) sensors and ground reaction force (GRF) sensors for locomotion mode recognition is presented, together with its use under the IoHT architecture. EMG and GRF information from ten healthy subjects in five common locomotion modes in daily life were collected, analyzed, and then transmitted to remote end terminals (e.g., personal computers). The data analysis process was implemented with machine learning techniques (Support Vector Machine), through which the locomotion modes were determined with a high accuracy of 96.38%. This article demonstrates a feasible means for accurate locomotion mode recognition by combining wearable sensing techniques and the machine learning algorithm, potentially advancing the development for IoHT based in-home rehabilitation.
KW - Flexible sensors
KW - In-home Rehabilitation
KW - Internet of Healthcare Things
KW - Locomotion mode recognition
UR - https://www.scopus.com/pages/publications/85099603212
U2 - 10.1109/FLEPS49123.2020.9239563
DO - 10.1109/FLEPS49123.2020.9239563
M3 - 会议稿件
AN - SCOPUS:85099603212
T3 - FLEPS 2020 - IEEE International Conference on Flexible and Printable Sensors and Systems
BT - FLEPS 2020 - IEEE International Conference on Flexible and Printable Sensors and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2020
Y2 - 16 August 2020 through 19 August 2020
ER -