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Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System

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Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 203))

Abstract

In the growing automation of existing world, activity modeling is being used in the field of technology to serve various purposes. One such field, which will be majorly benefited from daily activity modeling and life- living activities analysis, is monitoring of seasonal behavior pattern of elderly people, which can be further utilized in their remote health analysis and monitoring. Today’s demand is to develop a system with minimum human interaction and automatic anomaly detection and alert system. The proposed research work emphasizes to diagnose elderly persons daily behavioral patterns by observing their day-to-day routine activities with respect to time, location and context. To grow the accurateness of the structure, numerous sensing as well as actuator units have been deployed in elderly homes. Popular this research paper, we have recommended a unique sensing fusion technique to monitor seasonal, social, weather related and wellness observations of routine tasks. A novel daily activity learning model has been proposed which can record contextual data observations of various locations of a smart home and alert caretakers in the case of anomaly detection. We have analyzed monthly data of two old-aged smart homes with more than 5000 test samples. Results acquired from the investigation validate the accuracy and the efficiency of the proposed system which are recorded for 20 activities.

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Authors and Affiliations

  1. Symbiosis Center for Applied Artificial Intelligence and Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University, Pune, India

    Sharnil Pandya, Ketan Kotecha, Asif Ghanchi, Vedant Patadiya, Kuldeep Limbachiya & Anand Shivam

  2. Department of Computer Science Engineering, Ganpat University, Ahmedabad, Gujarat, India

    Mayur Mistry

  3. Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University, Pune, India

    Anirban Sur

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  1. Sharnil Pandya
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  2. Mayur Mistry
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  3. Ketan Kotecha
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Correspondence to Sharnil Pandya .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  4. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

  5. Instituto de Telecomunicações, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil

    Joel J. P. C. Rodrigues

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Pandya, S. et al. (2021). Smart Aging Wellness Sensor Networks: A Near Real-Time Daily Activity Health Monitoring, Anomaly Detection and Alert System. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_1

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