Handling Gait Impairments of Persons with Parkinson’s Disease by Means of Real-Time Biofeedback in a Daily Life Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9677)


A smartphone app with telemedicine capability integrating data from foot-mounted inertial measurement units (CuPiD-system) was developed to realize a portable gait analysis system and, on top of it, to provide people with Parkinson’s disease (PD) remote supervision and real-time feedback on gait performance. Eleven persons with PD were recommended to perform gait training for 30 min, three times per week for six weeks. The app offered praising/corrective verbal feedback, encouraging participants to keep the spatio-temporal gait parameters within a clinically determined ‘therapeutic window’. On average, persons performed 20 training sessions of 1.8 km in 24 min and received 28 corrective and 68 praising messages. The mean walking rhythm was 58 strides/min with a stride length of 1.28 m. System’s usability was determined as positive by the users. In conclusion, CuPiD resulted to be effective in promoting gait training in semi-supervised conditions, stimulating corrective actions and promoting self-efficacy to achieve optimal performance.


Parkinson’s disease Wearable sensors Android APP Tele-rehabilitation Biofeedback Gait 



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 288516 (CuPiD project).


  1. 1.
    Appelboom, G., Yang, A.H., Christophe, B.R., Bruce, E.M., Slomian, J., Bruyère, O., Bruce, S.S., Zacharia, B.E., Reginster, J.-Y., Connolly, E.S.: The promise of wearable activity sensors to define patient recovery. J. Clin. Neurosci. 21, 1089–1093 (2014)CrossRefGoogle Scholar
  2. 2.
    Shull, P.B., Jirattigalachote, W., Hunt, M.A., Cutkosky, M.R., Delp, S.L.: Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40, 11–19 (2014)CrossRefGoogle Scholar
  3. 3.
    Pasluosta, C.F., Gassner, H., Winkler, J., Klucken, J., Eskofier, B.M.: An emerging era in the management of Parkinson’s disease: wearable technologies and the internet of things. IEEE J. Biomed. Health Inform. 19, 1873–1881 (2015)CrossRefGoogle Scholar
  4. 4.
    Lowe, S.A., Ólaighin, G.: Monitoring human health behaviour in one’s living environment: a technological review. Med. Eng. Phys. 36, 147–168 (2014)CrossRefGoogle Scholar
  5. 5.
    Lee, Y.-S., Ho, C.-S., Shih, Y., Chang, S.-Y., Róbert, F.J., Shiang, T.-Y.: Assessment of walking, running, and jumping movement features by using the inertial measurement unit. Gait Posture 41, 877–881 (2015)CrossRefGoogle Scholar
  6. 6.
    Li, Y., Guo, Y.: Wiki-health: from quantified self to self-understanding. Future Gener. Comput. Syst. 56, 333–359 (2015)CrossRefGoogle Scholar
  7. 7.
    Lieber, B., Taylor, B.E.S., Appelboom, G., McKhann, G., Connolly, E.S.: Motion sensors to assess and monitor medical and surgical management of Parkinson disease. World Neurosurg. 84, 561–566 (2015)CrossRefGoogle Scholar
  8. 8.
    Qiang, J.K., Marras, C.: Telemedicine in Parkinson’s disease: a patient perspective at a tertiary care centre. Parkinsonism Relat. Disord. 21, 525–528 (2015)CrossRefGoogle Scholar
  9. 9.
    Nonnekes, J., Snijders, A.H., Nutt, J.G., Deuschl, G., Giladi, N., Bloem, B.R.: Freezing of gait: a practical approach to management. Lancet Neurol. 14, 768–778 (2015)CrossRefGoogle Scholar
  10. 10.
    Ginis, P., Nieuwboer, A., Dorfman, M., Ferrari, A., Gazit, E., Canning, C.G., Rocchi, L., Chiari, L., Hausdorff, J.M., Mirelman, A.: Feasibility and effects of home-based smartphone-delivered automated feedback training for gait in people with Parkinson’s disease: a pilot randomized controlled trial. Parkinsonism Relat. Disord. 22, 28–34 (2015)CrossRefGoogle Scholar
  11. 11.
    Lopez, W.O.C., Higuera, C.A.E., Fonoff, E.T., de Oliveira Souza, C.: O., Albicker, U., Martinez, J.A.E.: Listenmee and Listenmee smartphone application: synchronizing walking to rhythmic auditory cues to improve gait in Parkinson’s disease. Hum. Mov. Sci. 37, 147–156 (2014)CrossRefGoogle Scholar
  12. 12.
    Rochester, L., Baker, K., Hetherington, V., Jones, D., Willems, A.-M., Kwakkel, G., Van Wegen, E., Lim, I., Nieuwboer, A.: Evidence for motor learning in Parkinson’s disease: acquisition, automaticity and retention of cued gait performance after training with external rhythmical cues. Brain Res. 1319, 103–111 (2010)CrossRefGoogle Scholar
  13. 13.
    Nieuwboer, A., Kwakkel, G., Rochester, L., Jones, D., van Wegen, E., Willems, A.M., Chavret, F., Hetherington, V., Baker, K., Lim, I.: Cueing training in the home improves gait-related mobility in Parkinson’s disease: the RESCUE trial. J. Neurol. Neurosurg. Psychiatry 78, 134–140 (2007)CrossRefGoogle Scholar
  14. 14.
    Rocha, P.A., Porfírio, G.M., Ferraz, H.B., Trevisani, V.F.M.: Effects of external cues on gait parameters of Parkinson’s disease patients: a systematic review. Clin. Neurol. Neurosurg. 124, 127–134 (2014)CrossRefGoogle Scholar
  15. 15.
    Spaulding, S.J., Barber, B., Colby, M., Cormack, B., Mick, T., Jenkins, M.E.: Cueing and gait improvement among people with Parkinson’s disease: a meta-analysis. Arch. Phys. Med. Rehabil. 94, 562–570 (2013)CrossRefGoogle Scholar
  16. 16.
    Jones, D., Rochester, L., Birleson, A., Hetherington, V., Nieuwboer, A., Willems, A.-M., Van Wegen, E., Kwakkel, G.: Everyday walking with Parkinson’s disease: understanding personal challenges and strategies. Disabil. Rehabil. 30, 1213–1221 (2008)CrossRefGoogle Scholar
  17. 17.
    Rubinstein, T.C., Giladi, N., Hausdorff, J.M.: The power of cueing to circumvent dopamine deficits: a review of physical therapy treatment of gait disturbances in Parkinson’s disease. Mov. Disord. 17, 1148–1160 (2002)CrossRefGoogle Scholar
  18. 18.
    Goodwin, V.A., Richards, S.H., Taylor, R.S., Taylor, A.H., Campbell, J.L.: The effectiveness of exercise interventions for people with Parkinson’s disease: a systematic review and meta-analysis. Mov. Disord. 23, 631–640 (2008)CrossRefGoogle Scholar
  19. 19.
    Tomlinson, C.L., Patel, S., Meek, C., Herd, C.P., Clarke, C.E., Stowe, R., Shah, L., Sackley, C.M., Deane, K.H.O., Wheatley, K., Ives, N.: Physiotherapy versus placebo or no intervention in Parkinson’s disease. Cochrane Database Syst. Rev. 9, CD002817 (2013)Google Scholar
  20. 20.
    Lamotte, G., Rafferty, M.R., Prodoehl, J., Kohrt, W.M., Comella, C.L., Simuni, T., Corcos, D.M.: Effects of endurance exercise training on the motor and non-motor features of Parkinson’s disease: a review. J. Parkinsons Dis. 5, 21–41 (2015)Google Scholar
  21. 21.
    Schipper, K., Dauwerse, L., Hendrikx, A., Leedekerken, J.W., Abma, T.A.: Living with Parkinson’s disease: priorities for research suggested by patients. Parkinsonism Relat. Disord. 20, 862–866 (2014)CrossRefGoogle Scholar
  22. 22.
    Fok, P., Farrell, M., McMeeken, J., Kuo, Y.: The effects of verbal instructions on gait in people with Parkinson’s disease: a systematic review of randomized and non-randomized trials. Clin. Rehabil. 25, 396–407 (2011)CrossRefGoogle Scholar
  23. 23.
    Casamassima, F., Ferrari, A., Milosevic, B., Ginis, P., Farella, E., Rocchi, L.: A wearable system for gait training in subjects with Parkinson’s disease. Sens. (Basel) 14, 6229–6246 (2014)CrossRefGoogle Scholar
  24. 24.
    Ferrari, A., Ginis, P., Hardegger, M., Casamassima, F., Rocchi, L., Chiari, L.: A mobile kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters. IEEE Trans. Neural Syst. Rehabil. Eng. (2015). doi: 10.1109/TNSRE.2015.2457511 Google Scholar
  25. 25.
    CuPiD.: Closed-loop system for personalized and at-home rehabilitation of people with Parkinson’s disease.: FP7-ICT-288516, 2011–2014Google Scholar
  26. 26.
    Demers, L., Weiss-Lambrou, R., Ska, B.: Development of the Quebec user evaluation of satisfaction with assistive technology (QUEST). Assist. Technol. 8, 3–13 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electrical, Electronic and Information Engineering, “Guglielmo Marconi”University of BolognaBolognaItaly
  2. 2.Neuromotor Rehabilitation Research Group, Department of Rehabiliation SciencesKU LeuvenLeuvenBelgium
  3. 3.Oxford Computer Consultants Limited, OCCOxfordUK

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