An Open and Extensible Data Acquisition and Processing Platform for Rehabilitation Applications

  • Sehrizada SahinovicEmail author
  • Amina Dzebo
  • Baris Can Ustundag
  • Edin Golubovic
  • Tarik Uzunovic
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 59)


Recently we witnessed a great deal of progress in the field of medicine, as well as treatments that improve the patient therapy and care. However, physiotherapy and rehabilitation fields still face the challenges of treating patients in remote regions. Considering that, developing a data acquisition and processing platform that collects data of rehabilitation movements at home can play a key role in the success of a patient’s recovery process. The designed system is composed of three main parts: wearable sensor capable of collecting movement data with 3 axial accelerometer, gyroscope and magnetometer sensors, central hub for processing and a cloud system which is used as a link between the therapist and patient. The system was tested for purpose of monitoring rehabilitation exercises usually done during recovery from an elbow fracture. Experimental results have shown that the system presented in this paper gives successful results for rehabilitation applications.



Authors would like to acknowledge Inovatink ( for providing material and operational support in realization of this project.


  1. 1.
    O’Sullivan, S.B., Schmitz, T.J., Fulk, G.: Physical rehabilitation. FA Davis (2013)Google Scholar
  2. 2.
    Theodoros, D., Russell, T., Latifi, R.: Telerehabilitation: current perspectives. Stud. Health Technol. Inform. 131, 191–210 (2008)Google Scholar
  3. 3.
    Castro, H., Cha, E., Provance, P.G.: Home-based physical telerehabilitation in patients with multiple sclerosis: A pilot study. J. Rehabil. Res. Dev. 45(9), 1361 (2008)CrossRefGoogle Scholar
  4. 4.
    Thiers, A., Orteye, A., Orlowski, K., Schrader, T.: Technology in physical therapy. In Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 5, pp. 500–505. SCITEPRESS-Science and Technology Publications, Lda, March 2014Google Scholar
  5. 5.
    Levene, T., Steele, R.: The Quantified self and physical therapy: the application of motion sensing technologies. In: Proceedings of the International Conference on Compute and Data Analysis, pp. 263–267. ACM, May 2017Google Scholar
  6. 6.
    Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(1), 21 (2012)CrossRefGoogle Scholar
  7. 7.
    Hadjidj, A., Souil, M., Bouabdallah, A., Challal, Y., Owen, H.: Wireless sensor networks for rehabilitation applications: challenges and opportunities. J. Netw. Comput. Appl. 36(1), 1–15 (2013)CrossRefGoogle Scholar
  8. 8.
    Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Sig. Process. Control 3(1), 1–18 (2008)CrossRefGoogle Scholar
  9. 9.
    Caporuscio, M., Weyns, D., Andersson, J., Axelsson, C., Petersson, G.: IoT-enabled physical telerehabilitation platform. In: 2017 IEEE International Conference on Software Architecture Workshops (ICSAW), pp. 112–119. IEEE, April 2017Google Scholar
  10. 10.
    Bilic, D., Uzunovic, T., Golubovic, E., Ustundag, B.C.: Internet of things-based system for physical rehabilitation monitoring. In: 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, Bosnia and Herzegovina, pp. 1–6 (2017)Google Scholar
  11. 11.
    Dobkin, B.H.: A rehabilitation-internet-of-things in the home to augment motor skills and exercise training. Neurorehabil. Neural Repair 31(3), 217–227 (2017)CrossRefGoogle Scholar
  12. 12.
    Maksimović, M., Vujović, V.: Internet of things based e-health systems: ideas, expectations and concerns. In: Handbook of Large-Scale Distributed Computing in Smart Healthcare, pp. 241–280. Springer, Cham (2017)Google Scholar
  13. 13.
    Sevcenco, A.M., Li, K.F.: Motion tracking and learning in telerehabilitation applications. In: 2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), pp. 420–427. IEEE, November 2012Google Scholar
  14. 14.
    Moffet, H., Tousignant, M., Nadeau, S., Mérette, C., Boissy, P., Corriveau, H., Marquis, F., Cabana, F., Belzile, É.L., Ranger, P., Dimentberg, R.: Patient satisfaction with in-home telerehabilitation after total knee arthroplasty: results from a randomized controlled trial. Telemed. e-Health 23(2), 80–87 (2017)CrossRefGoogle Scholar
  15. 15.
    Liu, L., Miguel Cruz, A., Rios Rincon, A., Buttar, V., Ranson, Q., Goertzen, D.: What factors determine therapists’ acceptance of new technologies for rehabilitation–a study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disabil. Rehabil. 37(5), 447–455 (2015)CrossRefGoogle Scholar
  16. 16.
    Agostini, M., Moja, L., Banzi, R., Pistotti, V., Tonin, P., Venneri, A., Turolla, A.: Telerehabilitation and recovery of motor function: a systematic review and meta-analysis. J. Telemed. Telecare 21(4), 202–213 (2015)CrossRefGoogle Scholar
  17. 17.
    Wang, Q., Markopoulos, P., Yu, B., Chen, W., Timmermans, A.: Interactive wearable systems for upper body rehabilitation: a systematic review. J. Neuroeng. Rehabil. 14(1), 20 (2017)CrossRefGoogle Scholar
  18. 18.
    Han, S.L., Xie, M.J., Chien, C.C., Cheng, Y.C., Tsao, C.W.: Using MEMS-based inertial sensor with ankle foot orthosis for telerehabilitation and its clinical evaluation in brain injuries and total knee replacement patients. Microsyst. Technol. 22(3), 625–634 (2016)CrossRefGoogle Scholar
  19. 19.
    Meijer, H.A., Graafland, M., Goslings, J.C., Schijven, M.P.: A systematic review on the effect of serious games and wearable technology used in rehabilitation of patients with traumatic bone and soft tissue injuries. Archives of physical medicine and rehabilitation (2017)Google Scholar
  20. 20.
    Goncu-Berk, G., Topcuoglu, N.: A healthcare wearable for chronic pain management. Design of a smart glove for rheumatoid arthritis. Des. J. 20(Suppl. 1), S1978–S1988 (2017)Google Scholar
  21. 21.
    Tran, V., Lam, M.K., Amon, K.L., Brunner, M., Hines, M., Penman, M., Lowe, R., Togher, L.: Interdisciplinary eHealth for the care of people living with traumatic brain injury: a systematic review. Brain Injury 31(13–14), 1701–1710 (2017)CrossRefGoogle Scholar
  22. 22.
    Dobkin, B.H.: Rehabilitation strategies for restorative approaches after stroke and neurotrauma. In: Translational Neuroscience, pp. 539–553. Springer, Boston (2016)Google Scholar
  23. 23.
  24. 24.
    Lim, C.K., Chen, I.M., Luo, Z., Yeo, S.H.: A low cost wearable wireless sensing system for upper limb home rehabilitation. In: 2010 IEEE Conference on Robotics Automation and Mechatronics (RAM), pp. 1–8. IEEE, June 2010Google Scholar
  25. 25.
    Roggen, D., Pouryazdan, A., Ciliberto, M., BlueSense: designing an extensible platform for wearable motion sensing, sensor research and IoT applications. In: International Conference on Embedded Wireless Systems and Networks (2017)Google Scholar
  26. 26.
    González-Villanueva, L., Cagnoni, S., Ascari, L.: Design of a wearable sensing system for human motion monitoring in physical rehabilitation. Sensors 13(6), 7735–7755 (2013)CrossRefGoogle Scholar
  27. 27.
    Macedo, P., Afonso, J.A., Alexandre Rocha, L., Simões, R.: A telerehabilitation system based on wireless motion capture sensors. In: PhyCS (2014)Google Scholar
  28. 28.
  29. 29.
  30. 30.
  31. 31.
  32. 32.
  33. 33.
  34. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38.
  39. 39.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sehrizada Sahinovic
    • 1
    Email author
  • Amina Dzebo
    • 1
  • Baris Can Ustundag
    • 2
  • Edin Golubovic
    • 3
  • Tarik Uzunovic
    • 1
  1. 1.Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina
  2. 2.Department of Computer EngineeringIstanbul Technical UniversityIstanbulTurkey
  3. 3.InovatinkIstanbulTurkey

Personalised recommendations