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Computers in biology and medicine v.95, 2018년, pp.261 - 270   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Graph-based representation of behavior in detection and prediction of daily living activities

Augustyniak, Piotr (AGH University of Science and Technology, 30, Mickiewicz Ave, 30-059 Krakow, Poland ) ; Ślusarczyk, Grażyna (Jagiellonian University, 11, Łojasiewicza Str., 30-348 Kraków, Poland ) ;
  • 초록  

    Abstract Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a surveillance system designed for use in elderly smart homes. The description of a subject's activity is first decomposed into elementary poses - easily detectable by dedicated intelligent sensors - and represented by the share coefficients. Then, the activity is represented in the form of an attributed graph, where nodes correspond to elementary poses. As share coefficients of poses are expressed as attributes assigned to graph nodes, their change corresponding to a subject's action is represented by flow in graph edges. The behavioral record is thus a time series of graphs, which tiny size facilitates storage and management of long-term monitoring results. At the system learning stage, the contribution of elementary poses is accumulated, discretized and probability-ordered leading to a finite list representing the possible transitions between states. Such a list is independently built for each room in the supervised residence, and employed for assessment of the current action in the context of subject's habits and a room purpose. The proposed format of a behavioral record, applied to an adaptive surveillance system, is particularly advantageous for representing new activities not known at the setup stage, for providing a quantitative measure of transitions between poses and for expressing the difference between a predicted and actual action in a numerical way. Highlights Description of a subject's activity as a combination of easily detectable elementary poses. Support for activities not known at the setup stage. Compact yet flexible format of a behavioral record. Quantitative measure of transitions between poses (for mobility assessment). Quantitative measure of the difference between a predicted and actual action.


  • 주제어

    Behavior understanding .   Smart homes .   Assisted living .   Machine learning .   Graph-based structures.  

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