TY - GEN
T1 - An Activity Management System for Office Workers Using Multimodal Data
AU - Zhang, Xiangying
AU - Zheng, Pai
AU - He, Qiqi
AU - Peng, Tao
AU - Tang, Wangchujun
AU - Ye, Hongling
AU - Tang, Renzhong
N1 - Funding Information:
*This research was funded by the National Natural Science Foundation of China (Grant No. 72071179), Joint Supervision Scheme with the Chinese Mainland, Taiwan and Macao Universities-Zhejiang University, The Hong Kong Polytechnic University (Project code: G-SB2E), and ZJU-Sunon Joint Research Center of Smart Furniture, Zhejiang University. (#Corresponding author: Tao Peng, phone: +86-0571-87952048; fax: +86-0571-87951145; email: tao [email protected]) Xiangying Zhang is with the Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou, China, and is jointly-supervised with the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China (email: [email protected]).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/8
Y1 - 2022/8
N2 - Lacking a certain level of activity is associated with multiple health issues, and activity management is significant for office workers who sit 77% of working hours. Recent studies on the Internet of Things spur the advent of applications for daily activity management. However, there is no activity management system collecting data unobtrusively and continuously, which provides activity recognition and assessment for office workers. Hence, this study develops a multimodal activity management system based on an infrared array sensor placed on the desk, a sensing chair, and a mobile phone. This system contains data collection, activity recognition, and activity assessment. A deep learning algorithm based on the feature-level fusion strategy is leveraged to fuse the multimodal activity data and achieve recognition. The activity assessment considers energy expenditure and sedentary bout to reflect office workers' activity characteristics. Finally, an experiment is conducted to verify the feasibility of the proposed system. The results show that recognition accuracy can reach 99.9% and 83.9% by using the validation set approach and leave-one-subject-out cross-validation approach, respectively.
AB - Lacking a certain level of activity is associated with multiple health issues, and activity management is significant for office workers who sit 77% of working hours. Recent studies on the Internet of Things spur the advent of applications for daily activity management. However, there is no activity management system collecting data unobtrusively and continuously, which provides activity recognition and assessment for office workers. Hence, this study develops a multimodal activity management system based on an infrared array sensor placed on the desk, a sensing chair, and a mobile phone. This system contains data collection, activity recognition, and activity assessment. A deep learning algorithm based on the feature-level fusion strategy is leveraged to fuse the multimodal activity data and achieve recognition. The activity assessment considers energy expenditure and sedentary bout to reflect office workers' activity characteristics. Finally, an experiment is conducted to verify the feasibility of the proposed system. The results show that recognition accuracy can reach 99.9% and 83.9% by using the validation set approach and leave-one-subject-out cross-validation approach, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85141719525&partnerID=8YFLogxK
U2 - 10.1109/CASE49997.2022.9926455
DO - 10.1109/CASE49997.2022.9926455
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141719525
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1668
EP - 1673
BT - 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Y2 - 20 August 2022 through 24 August 2022
ER -