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Design and test of a Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during training of upper limb movement

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Abstract

The present paper describes the design and test of a low-cost Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during the execution of an upper limb exercise. Eleven sub-acute stroke patients with varying degrees of upper limb function were recruited. Each subject participated in a control session (repeated twice) and a feedback session (repeated twice). In each session, the subjects were presented with a rectangular pattern displayed on a vertical mounted monitor embedded in the table in front of the patient. The subjects were asked to move a marker inside the rectangular pattern by using their most affected hand. During the feedback session, the thickness of the rectangular pattern was changed according to the performance of the subject, and the color of the marker changed according to its position, thereby guiding the subject’s movements. In the control session, the thickness of the rectangular pattern and the color of the marker did not change. The results showed that the movement similarity and smoothness was higher in the feedback session than in the control session while the duration of the movement was longer. The present study showed that adaptive visual feedback delivered by use of the Kinect sensor can increase the similarity and smoothness of upper limb movement in stroke patients.

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References

  1. Cirstea MC, Levin MF (2007) Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. Neurorehabil Neural Repair 21:398–411. doi:10.1177/1545968306298414

    Article  CAS  PubMed  Google Scholar 

  2. Cirstea CM, Ptito A, Levin MF (2006) Feedback and cognition in arm motor skill reacquisition after stroke. Stroke 37:1237–1242

    Article  CAS  PubMed  Google Scholar 

  3. Colombo R, Pisano F, Mazzone A, Delconte C, Micera S, Carrozza MC, Dario P, Minuco G (2007) Design strategies to improve patient motivation during robot-aided rehabilitation. J. Neuroeng Rehabil 4:3. doi:10.1186/1743-0003-4-3

    Article  PubMed  PubMed Central  Google Scholar 

  4. Coote S, Murphy B, Harwin W, Stokes E (2008) The effect of the GENTLE/s robot-mediated therapy system on arm function after stroke. Clin Rehabil 22:395–405. doi:10.1177/0269215507085060

    Article  PubMed  Google Scholar 

  5. Dancause N, Ptito A, Levin MF (2002) Error correction strategies for motor behavior after unilateral brain damage: short-term motor learning processes. Neuropsychologia 40:1313–1323

    Article  PubMed  Google Scholar 

  6. DeJong SL, Schaefer SY, Lang CE (2012) The need for speed: better movement quality during faster task performance after stroke. Neurorehabil Neural Repair 26(362):373. doi:10.1177/1545968311425926

    Google Scholar 

  7. Heller A, Wade DT, Wood VA, Sunderland A, Hewer R, Ward E (1987) Arm function after stroke: measurement and recovery over the first three months. J Neurol Neurosurg Psychiatry 50:714–719

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jang SH, You SH, Hallett M, Cho YW, Park CM, Cho SH, Lee HY, Kim TH (2005) Cortical reorganization and associated functional motor recovery after virtual reality in patients with chronic stroke: an experimenter-blind preliminary study. Arch Phys Med Rehabil 86:2218–2223

    Article  PubMed  Google Scholar 

  9. Johansson RS, Flanagan JR (2009) Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci 10:345–359. doi:10.1038/nrn2621

    Article  CAS  PubMed  Google Scholar 

  10. Kleim JA, Jones TA (2008) Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res 51:225–239

    Article  Google Scholar 

  11. Levin MF, Kleim JA, Wolf SL (2009) What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke? Neurorehabil Neural Repair 23:313–319

    Article  CAS  PubMed  Google Scholar 

  12. Lyle RC (1981) A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res 4:483–492

    Article  CAS  PubMed  Google Scholar 

  13. Mathiowetz V, Weber K, Kashman N, Volland G (1985) Adult norms for the nine hole peg test of finger dexterity. Occup Ther J Res 5:24–38

    Article  Google Scholar 

  14. Maulucci RA, Eckhouse RH (2001) Retraining reaching in chronic stroke with real-time auditory feedback. NeuroRehabilitation 16:171–182

    CAS  PubMed  Google Scholar 

  15. Microsoft (2016) http://msdn.microsoft.com/en-us/library/jj131033.aspx. Accessed 25 Feb 2014

  16. Müller M (2007) Dynamic time warping. In: Information retrieval for music and motion, 1st edn. Springer, Berlin, pp 69–84

  17. Nakayama H, Jørgensen HS, Raaschou HO, Olsen TS (1994) Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil 75:394–398

    Article  CAS  PubMed  Google Scholar 

  18. Piron L, Tonin P, Piccione F, Iaia V, Trivello E, Dam M (2005) Virtual environment training therapy for arm motor rehabilitation. Presence 14:732–740

    Article  Google Scholar 

  19. Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, Stein J, Hogan N (2002) Movement smoothness changes during stroke recovery. J Neurosci 22:8297–8304

    CAS  PubMed  Google Scholar 

  20. Subramanian SK, Massie CL, Malcolm MP, Levin MF (2010) Does provision of extrinsic feedback result in improved motor learning in the upper limb poststroke? A systematic review of the evidence. Neurorehabil Neural Repair 24:113–124. doi:10.1177/1545968309349941

    Article  PubMed  Google Scholar 

  21. Sunderland A, Tinson D, Bradley L, Hewer R (1989) Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. J Neurol Neurosurg Psychiatry 52:1267–1272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Takeuchi N, Izumi SI (2012) Maladaptive plasticity for motor recovery after stroke: mechanisms and approaches. Neural Plast 2012:1–9. doi:10.1155/2012/359728

    Google Scholar 

  23. van Vliet PM, Wulf G (2006) Extrinsic feedback for motor learning after stroke: what is the evidence? Disabil Rehabil 28:831–840. doi:10.1080/09638280500534937

    Article  PubMed  Google Scholar 

  24. Ward NS, Cohen LG (2004) Mechanisms underlying recovery of motor function after stroke. Arch Neurol 61:1844–1848. doi:10.1001/archneur.61.12.1844

    Article  PubMed  PubMed Central  Google Scholar 

  25. Winstein CJ (1991) Knowledge of results and motor learning- implications for physical therapy. Phys Ther 71:140–149

    Article  CAS  PubMed  Google Scholar 

  26. Wulf G, Su J (2007) An external focus of attention enhances golf shot accuracy in beginners and experts. Res Q Exerc Sport 78:384–389. doi:10.1080/02701367.2007.10599436

    Article  PubMed  Google Scholar 

  27. Wulf G, Höß M, Prinz W (1998) Instructions for motor learning: differential effects of internal versus external focus of attention. J Mot Behav 30:169–179

    Article  CAS  PubMed  Google Scholar 

  28. Wulf G, Mcnevin NH, Shea CH (2001) The automaticity of complex motor skill learning as a function of attentional focus. Q J Exp Psychol 54:1143–1154. doi:10.1080/02724980143000118

    Article  CAS  Google Scholar 

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Acknowledgements

The Danish Research Council for Technology and Production supported the study.

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Correspondence to Ole Kæseler Andersen.

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Simonsen, D., Popovic, M.B., Spaich, E.G. et al. Design and test of a Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during training of upper limb movement. Med Biol Eng Comput 55, 1927–1935 (2017). https://doi.org/10.1007/s11517-017-1640-z

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