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Recognition of home activities for incomplete spinal cord injury areas utilizing models of hidden Markov simulation

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Abstract

Alerting investigators with practitioners for patient variability in the mobilization of activity undergone in the identification, recognition, and motor impairments subject to patient treatment enhancement. By adapting recognition models to populations, conventional practices associated with machine learning (ML) can enhance patient activity recognition (AR). Here, the supervised ML classifier is stationary, with the observations fed to the hidden Markov model (HMM). Also, empirically test the efficiency of our model with activities generated by the incomplete vision of ambulatory sessions in spinal cord injury (SCI). Walking, wheeling, lying down, sitting up, standing up, and stair climbing were among the exercises. The maximum classification accuracy using static classifiers alone using within-subject cross-validation was 86.3% (85.5–87.0, 95% confidence). We increased the accuracy to 88.9% by adding an HMM to the classification model (88.2–89.6). Compared to other classifiers, the proposed method outcome of an extra 2.8% showed considerable enhancement for precision classification when employing the classifier under hybridization and stationary characteristics. As a result of this improved activity identification, doctors may be able to choose or fine-tune the best physical or pharmacological therapy to increase patient mobility. Classifiers evaluated and trained in the laboratory utilizing within-subject cross-validation had a 91.6% accuracy rate. Conclusions Despite only a slight increase in activity levels, ambulatory people suffering from SCI enable to grow their ranges of Corticotropin-releasing factor (CRF) throughout the initial stage of inpatient recovery.

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Acknowledgements

The authors extend their appreciation and gratitude to Al-Mustaqbal University College in Iraq for funding this project.

Funding

This work is funded by Al-Mustaqbal University College in Iraq.

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Authors and Affiliations

  1. Department of ECE, Vardhaman College of Engineering Hyderabad, Hyderabad, India

    P. Kalyani

  2. Department of CSE, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India

    Y. Manasa

  3. Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, 51001, Hilla, Babil, Iraq

    Baraa Riyadh Altahan

  4. Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522302, India

    Sk. Hasane Ahammad & M. Suman

  5. Institute of Theoretical Electrical Engineering, Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, 44801, Bochum, Germany

    Md. Amzad Hossain

  6. Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh

    Md. Amzad Hossain

  7. Faculty of Electronic Engineering, Electronics and Electrical Communications Engineering Department, Menoufia University, Menouf, 32951, Egypt

    Ahmed Nabih Zaki Rashed

  8. Department of VLSI Microelectronics, Institute of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, 602105, India

    Ahmed Nabih Zaki Rashed

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  1. P. Kalyani
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  2. Y. Manasa
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  3. Baraa Riyadh Altahan
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  4. Sk. Hasane Ahammad
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  5. M. Suman
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  6. Md. Amzad Hossain
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  7. Ahmed Nabih Zaki Rashed
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All authors contributed equally to this work.

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Correspondence to Md. Amzad Hossain or Ahmed Nabih Zaki Rashed.

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Kalyani, P., Manasa, Y., Altahan, B.R. et al. Recognition of home activities for incomplete spinal cord injury areas utilizing models of hidden Markov simulation. SIViP 17, 3009–3017 (2023). https://doi.org/10.1007/s11760-023-02521-2

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  • DOI: https://doi.org/10.1007/s11760-023-02521-2

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