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An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in Internet of Medical Things environment

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Abstract

Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses batteryless or passive wearable body sensors for activity recognition, the data signals of these sensors have a high sparsity, which means that the time intervals of sensors’ readings are irregular and the number of sensors’ readings in time unit is frequently limited. Therefore, learning activity recognition models from temporally sparse data is challenging. Traditional machine learning techniques cannot be applicable in this scenario due to their focus on a single sensing modality requiring regular sampling rate. In this work, we propose an effective end-to-end deep neural network (DNN) model to recognize human activities from temporally sparse data signals of passive wearable sensors that improve the accuracy rate of human activity recognition. A dropout technique is used in the developed model to deal with sparsity problem and avoiding overfitting problem. In addition, optimization of the proposed DNN model was performed by evaluating a different number of hidden layers. Various experiments were conducted on a public clinical room dataset of sparse data signals to compare the performance of the proposed DNN model with the conventional and other deep learning approaches. The experimental results demonstrate that the proposed DNN model outperforms the existing state-of-the-art methods in terms of lower inference delay and activity recognition accuracy.

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Acknowledgements

This work was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-INF2885-02).

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Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mohammad Mehedi Hassan

  2. Department of Computer and Software Technology, University of Swat, Swat, 19130, Pakistan

    Sana Ullah

  3. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    M. Shamim Hossain & Abdulhameed Alelaiwi

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  1. Mohammad Mehedi Hassan
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  2. Sana Ullah
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  3. M. Shamim Hossain
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  4. Abdulhameed Alelaiwi
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Correspondence to Mohammad Mehedi Hassan.

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Hassan, M.M., Ullah, S., Hossain, M.S. et al. An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in Internet of Medical Things environment. J Supercomput 77, 2237–2250 (2021). https://doi.org/10.1007/s11227-020-03361-4

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