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Activity inference engine for real-time cognitive assistance in smart environments

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Abstract

Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants’ historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available.

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Notes

  1. For example, in linguistics, each subject establishes a binary relation with its object (or predictive) by the (linking) verb.

  2. Available at http://casas.wsu.edu/datasets/.

  3. Did not turn the water off, did not turn the burner off, did not bring the medicine container, did not use water to clean and did not dial a phone number.

  4. Dialed a wrong phone number and redialed, duplicate sampling of motion sensors, etc.

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Authors and Affiliations

  1. LIARA Laboratory, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, G7H 2B1, Canada

    Jianguo Hao, Abdenour Bouzouane, Bruno Bouchard & Sébastien Gaboury

Authors
  1. Jianguo Hao
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  2. Abdenour Bouzouane
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  3. Bruno Bouchard
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  4. Sébastien Gaboury
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Correspondence to Jianguo Hao.

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Hao, J., Bouzouane, A., Bouchard, B. et al. Activity inference engine for real-time cognitive assistance in smart environments. J Ambient Intell Human Comput 9, 679–698 (2018). https://doi.org/10.1007/s12652-017-0467-7

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  • DOI: https://doi.org/10.1007/s12652-017-0467-7

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