Monitoring Patient Recovery Using Wireless Physiotherapy Devices

  • Nirmalya RoyEmail author
  • Brooks Reed Kindle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8456)


We aim to improve physiotherapy patients’ recovery time by monitoring various prescribed tasks and displaying a score associated with how well the patient has performed said task. This kind of feedback would be desirable in situations where physical proximity between the physiotherapist and his patient is not always convenient or achievable. Having a way to remotely perform and receive feedback on prescribed tasks remedies that problem. We used a wireless device that contains accelerometer (acceleration) and gyroscope (angular velocity) sensors to collect motion information from the patient. After this information has been collected, it is processed in order to provide a more accurate representation of the performed task. The processed data is then broken up into micro-exercises, parts that make up the specified exercise, to evaluate qualitatively how accurately the exercise was performed and quantitatively how many times the task was performed. Finally, a task score is provided to the user that is based on the Functional Ability Scale and a weighted linear algorithm of the sum of the micro-exercise scores. This allows a patient to receive instant feedback on a performed task without the need to physically interact with a physiotherapist.



This work is supported by NSF-grants IIS-0647705 and CNS-1344990.


  1. 1.
    Patel, S., et al.: A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc. IEEE 98, 450–461 (2010)CrossRefGoogle Scholar
  2. 2.
    Patel, S., et al.: Monitoring motor fluctuations in patients with Parkinson disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13, 864–873 (2009)CrossRefGoogle Scholar
  3. 3.
    Wolf, S.L., et al.: Pilot normative database for the wolf motor function test. Arch. Phys. Med. Rehabil. 87, 443–445 (2006)CrossRefGoogle Scholar
  4. 4.
    SHIMMER (Sensing Health with Intelligence, Modularity, Mobility, and Experimental Reusability) sensor.
  5. 5.
    WEKA Data Mining Software.
  6. 6.
    Yan, Z., Chakraborty, D., Misra, A., Jeung, H., Aberer, K.: SAMMPLE: detecting semantic indoor activities in practical settings using locomotive signatures. In: International Symposium on Wearable Computers (ISWC) (2012)Google Scholar
  7. 7.
  8. 8.
    Hayes, T.L., Pavel, P., Larimer, N., Tsay, I.A., Nutt, J., Dami, A.G.: Distributed healthcare: simultaneous assessment of multiple individuals. IEEE Perv. Comput. 6(1), 36–43 (2007)CrossRefGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
    Phoenix Technologies Inc.
  12. 12.
    Reanex Technologies.
  13. 13.
    Simi Reality Motion Systems.
  14. 14.
    Varshney, U.: Pervasive healthcare and wireless health monitoring. Mobile Netw. Appl. 12(2–3), 113–127 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information SystemsUniversity of Maryland Baltimore CountyBaltimoreUSA
  2. 2.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA

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