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SHUBHCHINTAK

An efficient remote health monitoring approach for elderly people

  • 1211: AIoT Support and Applications with Multimedia
  • Published:
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Abstract

With the proliferation of IoT technology, it is anticipated that healthcare services, particularly for the elderly persons, will become a major thrust area of research in the coming days. Aim of this work is to design a fit-band containing multiple sensors to provide remote healthcare services for the elderly persons. An application has been designed to capture health data from the fit-band, pre-process the data and then send them to cloud for further analysis. A wireless Bluetooth enabled connection is proposed to establish communications between sensors and the application for data transmission. In the proposed application, there are three different front-end interfaces for three different users: system administrator, patient and doctor. The data collected from the patient’s fit-band are sent to a cloud data storage, where the data will be analyzed to detect anomaly (e.g., heart attack, sleep apnea, etc.). A Convolution Neural Network (CNN) model is proposed for anomaly detection. For the classification of anomaly, a Long Short Term Memory (LSTM) model is proposed. In the presence of anomaly, the system immediately connects a doctor through a phone call. A prototype system termed as Shubhchintak has been developed in Android/IOS environment and tested with a number of users. The fit-band provides data tracking with an overall accuracy of 99%; the system provides a response with 3000 requests in less than 100 ms. Also, Shubhchintak provides a real-time feedback with an accuracy of 97%. Shubhchintak is also tested by patients and doctors of a nearby hospital. Shubhchintak is shown to be a simple to use, cost effective, comfortable, and efficient system compared to the existing state of the art solutions.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, India

    Ayan Banerjee, Dibyendu Maji, Rajdeep Datta & Subhas Barman

  2. Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    Debasis Samanta

  3. Institute for Advancing Intelligence, TCG CREST, Salt Lake, Kolkata, 700091, India

    Samiran Chattopadhyay

  4. Department of Information Technology, Jadavpur University, Salt Lake City, Kolkata, 700106, India

    Samiran Chattopadhyay

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  1. Ayan Banerjee
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Correspondence to Subhas Barman.

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Banerjee, A., Maji, D., Datta, R. et al. SHUBHCHINTAK. Multimed Tools Appl 81, 37137–37163 (2022). https://doi.org/10.1007/s11042-022-13539-y

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