Recognizing Daily Human Activities Using Nonintrusive Sensing and Analytics for Supporting Human-Centered Built Environments

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.