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HumanSense: a framework for collective human activity identification using heterogeneous sensor grid in multi-inhabitant smart environments

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Abstract

Identification of human activity considering social interactions and group dynamics non-intrusively has been one of the fundamental problems and a challenging area of research. In real life, it is required for designing human-centric applications like assisted living, health care, and creating a smart home environment. As human beings spend 90% of time indoors, such a system will be helpful to monitor the behavioral anomalies of the inhabitants. Existing approaches have used intrusive or invasive methods like camera or wearable devices. In this work, we present a device-free, non-invasive, and non-intrusive sensing framework called HumanSense using an array of heterogeneous sensor grid for human activity monitoring. The sensor grids, comprising the ultrasonic and sound sensors, have been deployed for collective sensing combining a person’s physical activity and verbal interaction information. The proposed system senses a stream of events when the occupant(s) perform different physical activities categorized as atomic and group activities like sitting, standing, and walking. Simultaneously, it also tracks person-person verbal interactions such as monologue and discussion. Both information are then integrated into a single framework to understand the overall behavioral scenario of the indoor environment. The experimental results have shown that HumanSense can detect different activities with accuracy more than 90% and also improves overall identification accuracy compared to existing works. Our developed system can be further evolved into ready-to-deploy smart sensing panels which can be effective for human activity monitoring in an indoor environment.

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Authors and Affiliations

  1. Dr B C Roy Engineering College, Durgapur, India

    Arindam Ghosh

  2. Jadavpur University, Kolkata, India

    Amartya Chakraborty

  3. Tata Consultany Services Ltd., Chennai, India

    Joydeep Kumbhakar

  4. National Institute of Technology Durgapur, Durgapur, India

    Mousumi Saha & Sujoy Saha

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  1. Arindam Ghosh
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  2. Amartya Chakraborty
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  3. Joydeep Kumbhakar
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  4. Mousumi Saha
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  5. Sujoy Saha
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Correspondence to Arindam Ghosh.

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Ghosh, A., Chakraborty, A., Kumbhakar, J. et al. HumanSense: a framework for collective human activity identification using heterogeneous sensor grid in multi-inhabitant smart environments. Pers Ubiquit Comput 26, 521–540 (2022). https://doi.org/10.1007/s00779-020-01402-6

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