Using multiple sequence alignment to extract daily activity routines of the elderly living alone

  • Lee, Bogyeong (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Lee, Hyun-Soo (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Park, Moonseo (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Ahn, Changbum Ryan (Department of Construction Science, College of Architecture, Texas A&M University) ;
  • Choi, Nakjung (Nokia Bell labs) ;
  • Kim, Toseung (Department of Architecture and Architectural Engineering, Seoul National University)
  • Received : 2018.10.15
  • Accepted : 2019.02.04
  • Published : 2019.04.25


The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.



Supported by : Ministry of Land, Infrastructure and Transport


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