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Human Activity Behavioural Pattern Recognition in Smart Home with Long-Hour Data Collection

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Abstract

The research on human activity recognition has provided novel solutions to many applications like health care, sports, and user profiling. Considering the complex nature of human activities, it is still challenging even after effective and efficient sensors are available. The existing works on human activity recognition using smartphone sensors focus on recognizing basic human activities like sitting, slee**, standing, stair up and down, and running. However, more than these basic activities is needed to analyse human behavioural pattern. The proposed framework recognizes basic human activities using deep learning models. Also, ambient sensors like PIR, pressure sensors, and smartphone-based sensors like accelerometers and gyroscopes are combined to make it hybrid sensor-based human activity recognition. The hybrid approach helped derive more activities than the basic ones, which also helped derive human activity patterns or user profiling. User profiling provides sufficient information to identify daily living activity patterns and predict whether any anomaly exists. The framework provides the base for applications such as elderly monitoring when they are alone at home. The GRU model’s accuracy 95% is observed to recognize the basic activities. Finally, Human activity patterns over time are recognized based on the duration and frequency of the activities. It is observed that human activity pattern, like morning walking duration, varies depending on the day of the week.

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  1. Department of Information Technology, National Institute of Technology Karnataka, Srinivasnagar, Surathkal, Karnataka, 575025, India

    Ranjit Kolkar & V. Geetha

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  1. Ranjit Kolkar
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  2. V. Geetha
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This article is part of the topical collection “Research Trends in Communication and Network Technologies” guest edited by Anshul Verma, Pradeepika Verma and Kiran Kumar Pattanaik.

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Kolkar, R., Geetha, V. Human Activity Behavioural Pattern Recognition in Smart Home with Long-Hour Data Collection. SN COMPUT. SCI. 4, 864 (2023). https://doi.org/10.1007/s42979-023-02278-y

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