6th International Conference on Pervasive Computing Technologies for Healthcare

Research Article

AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2012.248600,
        author={Enamul Hoque and John Stankovic},
        title={AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities},
        proceedings={6th International Conference on Pervasive Computing Technologies for Healthcare},
        publisher={IEEE},
        proceedings_a={PERVASIVEHEALTH},
        year={2012},
        month={7},
        keywords={activity recognition smart home data mining wireless sensors},
        doi={10.4108/icst.pervasivehealth.2012.248600}
    }
    
  • Enamul Hoque
    John Stankovic
    Year: 2012
    AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2012.248600
Enamul Hoque1,*, John Stankovic1
  • 1: University of Virginia
*Contact email: eh6p@virginia.edu

Abstract

We present AALO: a novel Activity recognition system for single person smart homes using Active Learning in the presence of Overlapped activities. AALO applies data mining techniques to cluster in-home sensor firings so that each cluster represents instances of the same activity. Users only need to label each cluster as an activity as opposed to labeling all instances of all activities. Once the clusters are associated to their corresponding activities, our system can recognize future activities. To improve the activity recognition accuracy, our system preprocesses raw sensor data by identifying overlapping activities. The evaluation of activity recognition performance on a 26-day dataset shows that compared to Naive Bayesian (NB), Hidden Markov Model (HMM), and Hidden Semi Markov Model (HSMM) based activity recognition systems, our average time slice error (24.15%) is much lower than NB (53.04%), and similar to HMM (29.97%) and HSMM (26.29%). Thus, our active learning based approach performs as good as the state of the art supervised techniques (HMM and HSMM).