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An Upper Limb Rehabilitation Exercise Status Identification System Based on Machine Learning and IoT

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Abstract

Rapid increase in stroke incidence coupled with the high cost and limited availability of healthcare professionals has made stroke rehabilitation process inaccessible to many patients in developing countries. Stroke rehabilitation exercises are of critical importance in ensuring quick and lasting recovery. A machine learning-based system that can automatically identify the completion status of upper limb rehabilitation exercises is presented in this study. Proposed system can detect completion status of twelve hand and arm rehabilitation exercises: six hand, four forearm and two shoulder exercises. Accelerometers record the movement of the upper limb part being exercised in the form of data sequences. Six curve-fitting-based machine learning model types are trained and evaluated for their effectiveness in modelling these sequences. This resulted in a total of 216 models (i.e. 12 exercises × 3 axes of motion × 6 model types) being evaluated on three performance measures: SSE, RMSE and R2. Best-performing model for each exercise is plugged into the proposed exercise completion status identification algorithm. System effectiveness is validated using four performance measures: accuracy, precision, recall and F1-score. Proposed system is demonstrated to be capable of detecting the exercise completion status with up to 90% accuracy. The proposed system is connected to cloud via an IoT interface with a dashboard type visualization system that allows for the healthcare professionals to remotely monitor the progress for each patient as well as carry out offline analysis. A game interaction interface is also provided to enable interaction with video games, helping in higher patient engagement while performing exercises.

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References

  1. Pandian, J.D.; Sudhan, P.: Stroke epidemiology and stroke care services in India. J. Stroke 15, 128–134 (2013)

    Article  Google Scholar 

  2. Feigin, V.L.; Lawes, C.M.; Bennett, D.A.; Barker-Collo, S.L.; Parag, V.: Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol. 8, 355–369 (2009). https://doi.org/10.1016/S1474-4422(09)70025-0

    Article  Google Scholar 

  3. Giroud, M.; Jacquin, A.; Béjot, Y.: The worldwide landscape of stroke in the 21st century. Lancet 383, 195–197 (2014). https://doi.org/10.1016/S0140-6736(13)62077-2

    Article  Google Scholar 

  4. Banerjee, T.K.; Das, S.K.: Fifty years of stroke researches in India. Ann. Indian Acad. Neurol. 19, 1–8 (2016). https://doi.org/10.4103/0972-2327.168631

    Article  Google Scholar 

  5. Ferri, C.P.; Schoenborn, C.; Kalra, L.; Acosta, D.; Guerra, M.; Huang, Y.; Jacob, K.S.; Rodriguez, J.J.L.; Salas, A.; Sosa, A.L.; Williams, J.D.: Prevalence of stroke and related burden among older people living in Latin America, India and China. J. Neurol. Neurosurg. Psychiatry 82, 1074–1082 (2011)

    Article  Google Scholar 

  6. Kamalakannan, S.; Venkata, M.G.; Prost, A.; Pant, S.N.H.; Chitalurri, N.; Goenka, S.; Kuper, H.: Rehabilitation needs of stroke survivors after discharge from hospital in India. Arch. Phys. Med. Rehabil. 96, 1526–1532 (2016)

    Article  Google Scholar 

  7. Khan, F.R.; Vijesh, P.V.; Rahool, S.; Radha, A.A.; Kurupath, S.S.R.: Physiotherapy practice in stroke rehabilitation: a cross-sectional survey of physiotherapists in the state of Kerala, India. Top. Stroke Rehabil. 19, 405–410 (2012)

    Article  Google Scholar 

  8. Huang, V.S.; Krakauer, J.W.: Robotic neurorehabilitation: a computational motor learning perspective. J. Neuroeng. Rehabil. 6, 5 (2009)

    Article  Google Scholar 

  9. Megalingam, R.K.; Apuroop, K.G.S.; Boddupalli, S.: Single DoF hand orthosis for rehabilitation of stroke and SCI patients. In: International Conference on Materials, Alloys and Experimental Mechanics (ICMAEM-2017). pp. 1–6 (2017)

  10. Megalingam, R.K.; Baburaj, A.M.; Mattathil, A.; Hari, A.; Sreekanthan, K.; Koppaka, G.S.A.: Low cost hand orthotic device to improve hand function of stroke patients. Technol. Disabil. 30, 185–197 (2019). https://doi.org/10.3233/TAD-180197

    Article  Google Scholar 

  11. Teasell, R.W.: What’s new in stroke rehabilitation. Stroke 35, 383–385 (2004)

    Article  Google Scholar 

  12. Laver, K.; George, S.; Ratcliffe, J.; Crotty, M.: Virtual reality stroke rehabilitation - hype or hope? Aust. Occup. Ther. J. 58, 215–219 (2011)

    Article  Google Scholar 

  13. Sai Krishna, M.N.S.S.C.; Monesh Karthikkeyan, B.A.; Nair, B.B.; Rajagopalan, T.: Sensor-based grip strength monitoring system for stroke rehabilitation. In: Sengodan, T.; Murugappan, M.; Misra, S. (Eds.) Advances in Electrical and Computer Technologies, pp. 789–802. Springer Singapore, Singapore (2021)

    Chapter  Google Scholar 

  14. Amritha, N.; Mahima, M.M.; Namitha, K.; Unnikrishnan, R.; Harish, M.T.; Ravi, M.D.S.; Bhavani, R.R.: Design and development of balance training platform and games for people with balance impairments. In: 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016. pp. 960–966. IEEE (2016). https://doi.org/10.1109/ICACCI.2016.7732169

  15. Lin, W.-Y.; Chen, C.-H.; Tseng, Y.-J.; Tsai, Y.-T.; Chang, C.-Y.; Wang, H.-Y.; Chen, C.-K.: Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. Int. J. Med. Inform. 111, 159–164 (2018)

    Article  Google Scholar 

  16. Loya, A.; Deshpande, S.; Purwar, A.: Machine learning-driven individualized gait rehabilitation: classification, prediction, and mechanism design. J. Eng. Sci. Med. Diagn. Ther. 3, 021105 (2020)

    Google Scholar 

  17. Ongvisatepaiboon, K.; Chan, J.H.; Vanijja, V.: Smartphone-based tele-rehabilitation system for frozen shoulder using a machine learning approach. In: 2015 IEEE Symposium Series on Computational Intelligence. pp. 811–815. IEEE (2015)

  18. Beckerle, P.; Salvietti, G.; Unal, R.; Rossi, D.P.S.; Castellini, C.; Hirche, S.: A human–robot interaction perspective on assistive and rehabilitation robotics. Front. Neurorobot. 11, 24 (2017)

    Article  Google Scholar 

  19. Yang, G.; Deng, J.; Pang, G.; Zhang, H.; Li, J.; Deng, B.; Pang, Z.; Xu, J.; Jiang, M.; Liljeberg, P.; Xie, H.: An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning. EEE J. Transl. Eng. Heal. Med. 6, 1–10 (2018)

    Google Scholar 

  20. Eschmann, H.; Héroux, M.E.; Cheetham, J.H.; Potts, S.; Diong, J.: Thumb and finger movement is reduced after stroke: an observational study. PLoS ONE 14, 1–14 (2019). https://doi.org/10.1371/journal.pone.0217969

    Article  Google Scholar 

  21. Yu, C.-H.; Mathiowetz, V.: Efficacy of a forearm rotation orthosis for persons with a hemiparetic arm: study protocol, University of Minnesota-Minneapolis (2017)

  22. Ratanapinunchai, J.; Mathiyakom, W.; Sungkarat, S.: Scapular upward rotation during passive humeral abduction in individuals with hemiplegia post-stroke. Ann. Rehabil. Med. 43, 178–186 (2019). https://doi.org/10.5535/arm.2019.43.2.178

    Article  Google Scholar 

  23. Margot, A.; Hoessly, M.; Hedges, K.: Your guide to exercise after a stroke: a guide for people with stroke and their families. Stroke Foundation - New Zealand (2017)

  24. Bonnyaud, C.; Jinwala, K.; Roche, N.: Self-Rehabilitation Booklet for Hemiplegic Patients. Fondation Garches-Allergan Inc., Garches (2015)

    Google Scholar 

  25. Dept.of.Rehabilitation: Arm Exercises for Stroke Patients. Singapore (2021)

  26. Kim, W.S.; Cho, S.; Baek, D.; Bang, H.; Paik, N.J.: Upper extremity functional evaluation by Fugl-Meyer assessment scoring using depth-sensing camera in hemiplegic stroke patients. PLoS ONE 11, 1–13 (2016). https://doi.org/10.1371/journal.pone.0158640

    Article  Google Scholar 

  27. Noorkõiv, M.; Rodgers, H.; Price, C.I.: Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. J. Neuroeng. Rehabil. (2014). https://doi.org/10.1186/1743-0003-11-144

    Article  Google Scholar 

  28. MacEira-Elvira, P.; Popa, T.; Schmid, A.C.; Hummel, F.C.: Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J. Neuroeng. Rehabil. 16, 1–18 (2019). https://doi.org/10.1186/s12984-019-0612-y

    Article  Google Scholar 

  29. STMicroelectronics: LSM6DS33 iNEMO inertial module. (2017)

  30. Hsiao, Y.Y.; Huang, C.C.: Characterization of hand tendon by using high frequency ultrasound imaging. In: Jarm, T.; Mahnič-Kalamiza, S.; Cvetkoska, A.; Miklavčič, D. (Eds.) 8th European Medical and Biological Engineering Conference (EMBEC 2020). p. 92. Springer, Cham, Portoroz, Slovenia (2020)

  31. Halloran, S.; Tang, L.; Guan, Y.; Shi, J.Q.; Eyre, J.: Remote monitoring of stroke patients’ rehabilitation using wearable accelerometers. In: Proceedings - International Symposium on Wearable Computers, ISWC. pp. 72–77. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3341163.3347731

  32. NIST: NIST/SEMATECH e-Handbook of Statistical Methods. National Institute of Standards and Technology -U.S Department of Commerce (2013). https://doi.org/10.18434/M32189

  33. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)

    Article  MathSciNet  Google Scholar 

  34. Nair, B.B.; Patturajan, M.; Mohandas, V.P.; Sreenivasan, R.R.: Predicting the BSE sensex: performance comparison of adaptive linear element, feed forward and time delay neural networks. In: 2012 International Conference on Power, Signals, Controls and Computation, EPSCICON 2012. pp. 290–293 (2012). https://doi.org/10.1109/EPSCICON.2012.6175277

  35. MathWorks: Gaussian Models, https://in.mathworks.com/help/curvefit/gaussian.html, last Accessed from 29 May 2021

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Funding

The research leading to these results received funding from Department of Science and Technology, India, under Technology intervention for disabled and elderly (TIDE) scheme. The authors gratefully acknowledge the financial support provided by DST.

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Authors and Affiliations

  1. SIERS Research Laboratory, Department Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Binoy B. Nair

  2. Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    N. R. Sakthivel

Authors
  1. Binoy B. Nair
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  2. N. R. Sakthivel
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Binoy B Nair and Dr. N R Sakthivel. The first draft of the manuscript was written by Dr. Binoy B Nair and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Binoy B. Nair.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

Appendix 1

Appendix 1

Tables

Table 16 Parameters of the model selected for hand exercise-1
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Table 17 Parameters of the model selected for hand exercise-2
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Table 18 Parameters of the model selected for hand exercise-3
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Table 19 Parameters of the model selected for hand exercise-4
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Table 20 Parameters of the model selected for hand exercise-5
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Table 21 Parameters of the model selected for hand exercise-6
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Table 22 Parameters of the model selected for forearm exercise-1
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Table 23 Parameters of the model selected for forearm exercise-2
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Table 24 Parameters of the model selected for forearm exercise-3
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Table 25 Parameters of the model selected for forearm exercise-4
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Table 26 Parameters of the model selected for shoulder exercise-1
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Table 27 Parameters of the model selected for shoulder exercise-2
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Nair, B.B., Sakthivel, N.R. An Upper Limb Rehabilitation Exercise Status Identification System Based on Machine Learning and IoT. Arab J Sci Eng 47, 2095–2121 (2022). https://doi.org/10.1007/s13369-021-06152-y

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