TY - GEN
T1 - Recognizing Daily Human Activities Using Nonintrusive Sensing and Analytics for Supporting Human-Centered Built Environments
AU - Huang, Kaiyu
AU - Liu, Kaijian
N1 - Publisher Copyright:
© 2024 ASCE.
PY - 2024
Y1 - 2024
N2 - Recognizing daily human activities offers a great promise to develop human-centered efficient, assistive, and healthy built environments. However, the state-of-the-art sensing methods for human activity recognition are mostly intrusive: they either rely on capturing private personal information or require humans to wear sensors. Such intrusive sensing often raises privacy concerns or suffers from adherence problems (i.e., people stop wearing the sensors with time). There is, thus, a need for a nonintrusive sensing method to better support daily activity recognition in buildings. To address this need, this paper proposes a novel nonintrusive sensing and analytics method. At the cornerstone of the proposed method is a new multi-purpose sensing system, which captures the composition changes of multiple indoor gases induced by daily activities, without capturing private occupant information and requiring sensor wearing, for supporting activity recognition. As a pilot study, this paper focuses on evaluating the feasibility of the proposed nonintrusive sensing method by testing the significance of the differences in air composition data collected under different daily activities (e.g., cooking, sleeping, and idling). The experimental results show the feasibility of the proposed method to recognize daily human activities in a nonintrusive way.
AB - Recognizing daily human activities offers a great promise to develop human-centered efficient, assistive, and healthy built environments. However, the state-of-the-art sensing methods for human activity recognition are mostly intrusive: they either rely on capturing private personal information or require humans to wear sensors. Such intrusive sensing often raises privacy concerns or suffers from adherence problems (i.e., people stop wearing the sensors with time). There is, thus, a need for a nonintrusive sensing method to better support daily activity recognition in buildings. To address this need, this paper proposes a novel nonintrusive sensing and analytics method. At the cornerstone of the proposed method is a new multi-purpose sensing system, which captures the composition changes of multiple indoor gases induced by daily activities, without capturing private occupant information and requiring sensor wearing, for supporting activity recognition. As a pilot study, this paper focuses on evaluating the feasibility of the proposed nonintrusive sensing method by testing the significance of the differences in air composition data collected under different daily activities (e.g., cooking, sleeping, and idling). The experimental results show the feasibility of the proposed method to recognize daily human activities in a nonintrusive way.
UR - http://www.scopus.com/inward/record.url?scp=85188669528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85188669528&partnerID=8YFLogxK
U2 - 10.1061/9780784485262.041
DO - 10.1061/9780784485262.041
M3 - Conference contribution
AN - SCOPUS:85188669528
T3 - Construction Research Congress 2024, CRC 2024
SP - 397
EP - 405
BT - Advanced Technologies, Automation, and Computer Applications in Construction
A2 - Shane, Jennifer S.
A2 - Madson, Katherine M.
A2 - Mo, Yunjeong
A2 - Poleacovschi, Cristina
A2 - Sturgill, Roy E.
T2 - Construction Research Congress 2024, CRC 2024
Y2 - 20 March 2024 through 23 March 2024
ER -