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The prototype of wearable sensors system for supervision of patient rehabilitation using artificial intelligence methods

  • Eliasz KántochEmail author
  • Dominik Grochala
  • Marcin Kajor
  • Dariusz Kucharski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 623)

Abstract

In this paper, we investigate a wearable multi-sensor system for telemedical supervision of patient rehabilitation in home conditions. Our approach is based on a set of wearable sensors connected with a master digital acquisition module. Proposed platform is capable of recording a single-lead ECG and acceleration signals which are used for determining the patient’s activity form. During research we examined the following activities of daily living: sitting (idle), walking and squatting. A set of machine learning methods were used for classification of mentioned activities as rehabilitation exercises. Our methodology showed high success rates (above 92%) for detecting selected activities and it showed great potential to be used for patient’s activity recognition.

Keywords

wearable system body sensor network biomedical sensors telemedicine patient rehabilitation activity classification 

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References

  1. 1.
    Chan, M., EstÃĺve, D., Fourniols, J.-Y., Escriba, C., Campo, E.: Smart wearable systems: Current status and future challenges. Artif Intell Med. 56, 137–156 (2012).Google Scholar
  2. 2.
    Klingeberg, T., Schilling, M.: Mobile wearable device for long term monitoring of vital signs. Comput. Methods Programs Biomed. 106, 89–96 (2012).Google Scholar
  3. 3.
    Kantoch, E., Ieee: Technical Verification of applying Wearable Physiological Sensors in Ubiquitous Health Monitoring. In: 2013 Computing in Cardiology Conference. pp. 269–272 (2013).Google Scholar
  4. 4.
    Kańtoch, E.: Telemedical human activity monitoring system based on wearable sensors network. In: 41st Computing in Cardiology Conference, CinC 2014. pp. 469–472 (2014).Google Scholar
  5. 5.
    Huang, B., Giggins, O., Kechadi, T., Caulfield, B.: The limb movement analysis of rehabilitation exercises using wearable inertial sensors. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 4686–4689. IEEE (2016).Google Scholar
  6. 6.
    Lu, J., Zhang, T., Sun, Q., Kadiwal, S., Unwala, I., Hu, F.: Monitoring of Paces and Gaits Using Binary PIR Sensors with Rehabilitation Treadmill. Med. . . . . 1, 5315–5318 (2016).Google Scholar
  7. 7.
    Saracino, L., Ruffaldi, E., Graziano, A., Avizzano, C.A.: Fusion of wearable sensors and mobile haptic robot for the assessment in upper limb rehabilitation, (2016).Google Scholar
  8. 8.
    Pierleoni, P., Belli, A., Maurizi, L., Palma, L., Pernini, L., Paniccia, M., Valenti, S.: A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor, (2016).Google Scholar
  9. 9.
    Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Inf. Fusion. 35, 68–80 (2017).Google Scholar
  10. 10.
    NEITZEL, Jennifer A.; DAVIES, George J. The Benefits and Controversy of the Parallel Squat in Strength Training and Rehabilitation. Strength & Conditioning Journal, 2000, 22.3: 30.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Eliasz Kántoch
    • 1
    Email author
  • Dominik Grochala
    • 1
  • Marcin Kajor
    • 1
  • Dariusz Kucharski
    • 1
  1. 1.AGH University of Science and TechnologyKrakówPoland

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