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CNN-based device-free health monitoring and prediction system using WiFi signals

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Abstract

Health monitoring has been a growing area of interest in recent times. It provides many beforehand benefits including early detection of potentially harmful illnesses. Till now, it has been achieved primarily by using devices such as wearable bands to monitor breathing rate and heart rate. Wearing and carrying devices make the person uncomfortable. In this paper, we introduce a deep learning based device-free way to monitor breathing rate and heart rate in different human positions to provide insight into whether the person is healthy. A convolutional neural network (CNN) model along with Gaussian naive bayes (GaussianNB) and support vector machine (SVM) is introduced and the device-free monitoring is implemented using WiFi signals. An individual in proximity causes a change in the channel state information (CSI) and the distortion is measured to provide corresponding vitals. The vitals give an insight into the health of the individual. The experimental results are quite encouraging with the evidence of superiority of SVM with CNN.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology Kota, Jaipur, India

    Amit Kumar, Shashwat Singh, Vibhor Rawal & Shivam Garg

  2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology Kota, Jaipur, India

    Anand Agrawal

  3. Department of Computer Science and Engineering, National Institute of Technology, Raipur, India

    Sonal Yadav

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  1. Amit Kumar
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  2. Shashwat Singh
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  3. Vibhor Rawal
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  4. Shivam Garg
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  5. Anand Agrawal
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  6. Sonal Yadav
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Correspondence to Amit Kumar.

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Kumar, A., Singh, S., Rawal, V. et al. CNN-based device-free health monitoring and prediction system using WiFi signals. Int. j. inf. tecnol. 14, 3725–3737 (2022). https://doi.org/10.1007/s41870-022-01023-7

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  • DOI: https://doi.org/10.1007/s41870-022-01023-7

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