TY - JOUR
T1 - WMS
T2 - Wearables-Based Multisensor System for In-Home Fitness Guidance
AU - Liang, Liwen
AU - Duan, Yuxuan
AU - Che, Jincheng
AU - Tang, Chenyu
AU - Dai, Wensi
AU - Gao, Shuo
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Human activity recognition (HAR) is now a powerful in-home fitness assistive technology. This article presents a wearables-based multisensor system (WMS), which not only supports conventional functionalities, such as motion evaluation based on multidimensional information about the user's body (movement speed, angle, muscle states, etc.), but also provides advanced services, including assessing training fatigue and providing real time, elastic, and professional training advice. The proposed WMS is experimentally validated by yielding 90.11% accuracy of motion evaluation with 36% and 23% improvement of fitness effect on bicep girth and muscular endurance, indicating its feasibility to prompt the development of HAR in the in-home fitness training domain.
AB - Human activity recognition (HAR) is now a powerful in-home fitness assistive technology. This article presents a wearables-based multisensor system (WMS), which not only supports conventional functionalities, such as motion evaluation based on multidimensional information about the user's body (movement speed, angle, muscle states, etc.), but also provides advanced services, including assessing training fatigue and providing real time, elastic, and professional training advice. The proposed WMS is experimentally validated by yielding 90.11% accuracy of motion evaluation with 36% and 23% improvement of fitness effect on bicep girth and muscular endurance, indicating its feasibility to prompt the development of HAR in the in-home fitness training domain.
KW - Human activity recognition (HAR)
KW - multisensor data fusion
KW - supervised neural network
KW - wearables
UR - https://www.scopus.com/pages/publications/85159805966
U2 - 10.1109/JIOT.2023.3274831
DO - 10.1109/JIOT.2023.3274831
M3 - 文章
AN - SCOPUS:85159805966
SN - 2327-4662
VL - 10
SP - 17424
EP - 17435
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 19
ER -