Detection of daily living activities using a two-stage Markov model

  • Authors:

  • Love Kalra;Xinghui Zhao;Axel J. Soto;Evangelos Milios

  • Affiliations:

  • Faculty of Computer Science, Dalhousie University, 6050 University Ave., PO BOX 15000, Halifax, NS, Canada B3H4R2. E-mail: love.kalra@dal.ca, {soto,eem}@cs.dal.ca;School of Engineering and Computer Science, Washington State University Vancouver, 14204 NE Salmon Creek Ave., Vancouver, WA 98686, USA. E-mail: x.zhao@wsu.edu;Faculty of Computer Science, Dalhousie University, 6050 University Ave., PO BOX 15000, Halifax, NS, Canada B3H4R2. E-mail: love.kalra@dal.ca, {soto,eem}@cs.dal.ca;Faculty of Computer Science, Dalhousie University, 6050 University Ave., PO BOX 15000, Halifax, NS, Canada B3H4R2. E-mail: love.kalra@dal.ca, {soto,eem}@cs.dal.ca

  • Venue:
  • Journal of Ambient Intelligence and Smart Environments - Intelligent agents in Ambient Intelligence and smart environments
  • Year:
  • 2013

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Abstract

A supervised statistical model for detecting the activities of daily living ADL from sensor data streams is proposed in this paper. This method works in two stages aiming at capturing temporal intra-and inter-activity relationships. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features within the activities. A novel data segmentation approach is proposed for accurate inferencing at the first stage. To boost the accuracy, a second stage consisting of a Hidden Markov Model is added that serves two purposes. Firstly, it acts as a corrective stage, as it learns the probability of each activity being incorrectly inferred by the first stage, so that they can be corrected at the second stage. Secondly, it introduces inter-activity transition information to capture possible time-dependent relationships between two contiguous activities. We applied our method to three smart house datasets. Comparison of the results to other traditional approaches for ADL identification shows competitive or better performance. The paper also proposes a deployment of our methodology using an agent-based architecture.