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A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note

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Abstract

Static and dynamic posture analysis was a critical clinical examination in physiotherapy and rehabilitation. It was a time-consuming task for clinicians, so a semi-automatic method can facilitate this process as well as provide well-documented medical records and strong infrastructure for deep learning scenarios. The current research presents a mechatronics platform for static and real-time dynamic posture analysis, which consisted of hybrid computational modules. Our study was a developmental and applied research according to a system development life cycle. The designed modules are as follows: (1) a mechanical structure includes patient place, 360-degree engine, mirror, laser, distance meter, and cams; (2) a software module includes data collection, electronic medical record, semi-automatic image analysis, annotation, and reporting, and (3) a network to exchange raw data with deep learning server. Patients were informed about the research by their healthcare provider and all data were transformed into a Fourier format, in which the patients remained autonomous without a bit of information. The results show acceptable reliability and validity of the instruments. Also, a telerehabilitation application was designed to cover the patients after diagnosis. We suggest a longer time for data acquisition. It will lead to a more accurate and fully automated dynamic posture analysis. The result of this study suggest that the designed mechatronics device used in conjunction with smartphone application is a valid tool that can be used to obtain reliable measurements.

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Availability of data and material

The source code, demo, and early evaluation results can be provided as needed.

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Funding

These experiments were supported by SanamSahand.com Knowledge-Based company as a startup activity under the Iranian vice presidency for science and technology license (Grant No: 35401456).

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Authors and Affiliations

  1. Physiotherapy Department, Faculty of Rehabilitation, Tabriz University of Medical Science, 29 Bahman St, Tabriz, Iran

    Zahra Salahzadeh

  2. Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran

    Peyman Rezaei-Hachesu, Ali Aghamali & Taha Samad-Soltani

  3. Department of Artificial Intelligence, Faculty of Computer Engineering, University of Tabriz, Tabriz, Iran

    Yousef Gheibi

  4. Department of Research and Development, SanamSahand Health Promotion Industries, Tabriz, Iran

    Hamed Pakzad

  5. Department of Biomedical Engineering, Islamic Azad University of Tabriz, Tabriz, Iran

    Saeideh Foladlou

Authors
  1. Zahra Salahzadeh
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  2. Peyman Rezaei-Hachesu
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  3. Yousef Gheibi
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  4. Ali Aghamali
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  5. Hamed Pakzad
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  6. Saeideh Foladlou
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  7. Taha Samad-Soltani
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Contributions

All authors contributed equally.

Corresponding author

Correspondence to Taha Samad-Soltani.

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Conflict of interest

There is no conflict of interest.

Ethical Statements

All procedures performed in the study were in accordance with ethical and technical standards. The Iranian national medical device directorate(IMED) checked and validated the structure of the device. Also, it received a national patent certificate.

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The source code, demo, and early evaluation results can be provided as needed.

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Salahzadeh, Z., Rezaei-Hachesu, P., Gheibi, Y. et al. A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note. Phys Eng Sci Med 44, 901–910 (2021). https://doi.org/10.1007/s13246-021-01035-w

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  • DOI: https://doi.org/10.1007/s13246-021-01035-w

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