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Predicting Activities of Daily Living with Spatio-Temporal Information

Author

Listed:
  • Sook-Ling Chua

    (Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia)

  • Lee Kien Foo

    (Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia)

  • Hans W. Guesgen

    (School of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand)

Abstract

The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.

Suggested Citation

  • Sook-Ling Chua & Lee Kien Foo & Hans W. Guesgen, 2020. "Predicting Activities of Daily Living with Spatio-Temporal Information," Future Internet, MDPI, vol. 12(12), pages 1-13, November.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:214-:d:452542
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    Cited by:

    1. Filipe Portela, 2021. "Data Science and Knowledge Discovery," Future Internet, MDPI, vol. 13(7), pages 1-4, July.

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