Dense Sensing Based Composite Activity Recognition in Smart Home
In terms of context aware services corresponding to Activity of Daily Living (ADL) with uncertainty and multitasking, Markov Logic Networks (MLN) is proved as an effective solution in sequential Human Activity Recognition (HAR) in Smart Home. However, it still remains a challenge to carry out real time progressive composite activity recognition in interleaved and/or concurrent manner. To solve this problem, this paper proposes a novel approach named CCAR (Continuous Composite Activity Recognition) to recognize composite activity via real time sensor data segmentation. First, an interleaved event sequence is segmented into simple sequential fragments using event correlation and feedback mechanism, which make each segmented fragments correspond to one ongoing activity. Second, these sequential fragments are inferred with Maximum A Posterior (MAP) in MLN, so that the time intervals about various recognized simple activities are stored into the activity timeline table (ATT) Third, the composite activities can be recognized from ATT according to Allen Temporal Logic. Lastly, by evaluating with CASAS interleaved ADL datasets, the experimental result reveals that the proposed method has a high accuracy rate for composite activities.
2018 10th International Conference on Intelligent Human Machine Systems and Cybernetics (IHMSC)