TY - GEN
T1 - Poster
T2 - 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024
AU - Choi, Hyuckjin
AU - Nakamura, Yugo
AU - Fukushima, Shogo
AU - Arakawa, Yutaka
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/6/3
Y1 - 2024/6/3
N2 - Since office work has become large-scale and diversified in companies or organizations, work engagement and efficiency have been always an important index of a team's or group's evaluation because it is directly connected to their outcomes. In order to identify the group work context, we first need to recognize for what and how long the individual members are spending their time at their desks, but without privacy concerns and underestimation of their actual work. In this paper, we propose and evaluate the base system of personal desk activity recognition by using a low-cost compact WiFi node and its WiFi channel state information (CSI), which can lead to a lightweight group work context identification system. As a result, we achieved 94.2% desk activity recognition accuracy using the on-desk receiver, in recognizing five different classes.
AB - Since office work has become large-scale and diversified in companies or organizations, work engagement and efficiency have been always an important index of a team's or group's evaluation because it is directly connected to their outcomes. In order to identify the group work context, we first need to recognize for what and how long the individual members are spending their time at their desks, but without privacy concerns and underestimation of their actual work. In this paper, we propose and evaluate the base system of personal desk activity recognition by using a low-cost compact WiFi node and its WiFi channel state information (CSI), which can lead to a lightweight group work context identification system. As a result, we achieved 94.2% desk activity recognition accuracy using the on-desk receiver, in recognizing five different classes.
KW - desk works
KW - human activity recognition
KW - wifi channel state information
UR - http://www.scopus.com/inward/record.url?scp=85196220783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196220783&partnerID=8YFLogxK
U2 - 10.1145/3643832.3661452
DO - 10.1145/3643832.3661452
M3 - Conference contribution
AN - SCOPUS:85196220783
T3 - MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services
SP - 702
EP - 703
BT - MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services
PB - Association for Computing Machinery, Inc
Y2 - 3 June 2024 through 7 June 2024
ER -
