| Title |
Behavioral Contextualization for Extracting Occupant's ADL Patterns in Smart-home Environment |
| Authors |
Lee, Bogyeong ; Lee, Hyun-Soo ; Park, Moonseo |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2018.19.1.021 |
| Keywords |
Activities of Daily Living (ADL) ; Behavioral Contextualization ; Non-intrusive Sensing Approach ; Smart-home |
| Abstract |
The rapid increase of the elderly living alone is a critical issue in worldwide as it leads to a rapid increase of a social support costs (e.g., medical expenses) for the elderly. In early stages of dementia, the activities of daily living (ADL) including self-care tasks can be affected by abnormal patterns or behaviors and used as an evidence for the early diagnosis. However, extracting activities using non-intrusive approach is still quite challenging and the existing methods are not fully visualized to understand the behavior pattern or routine. To address these issues, this research suggests a model to extract the activities from coarse-grained data (spatio-temporal data log) and visualize the behavioral context information. Our approach shows the process of extracting and visualizing the subject's space-activity map presenting the context of each activity (time, room, duration, sequence, frequency). This research contributes to show a possibility of detecting subject's activities and behavioral patterns using coarse-grained data (limited to spatio-temporal information) with little infringement of personal privacy. |