Serious Games and Virtual Reality (VR) are being considered at present as an alternative to traditional rehabilitation therapies. In this paper, the ongoing development of a framework focused on rehabilitation and assessment of the upper limb motor function based on serious games as a source of entertainment for physiotherapy patients is described. A set of OpenSource Serious Games for rehabilitation has been developed, using the last version of Microsoft1® Kinect™ as low cost monitoring sensor and the software Unity. These Serious Games captures 3D human body data and it stored them in the patient database to facilitate a later clinical analysis to the therapist. Also, a VR-based system for the automated assessment of motor function based on Fugl-Meyer Assessment Test (FMA) is addressed. The proposed system attempts to be an useful therapeutic tool for tele-rehabilitation in order to reduce the number of patients, time spent and cost to
Biomechanical analysis is an important feature during the evaluation and clinical diagnosis of motor deficits caused by traumas or neurological diseases. For that reason Motion capture (MoCap) systems are widely used in biomechanical studies, in order to collect position data from anatomical landmarks with high accuracy. Their results are used to estimate joint movements, positions, and muscle forces. These quantitative results improve the tracking of changes in motor functions over time, being more accurately than clinical ratings . For clinical applications, these results are usually transformed into clinically meaningful and interpretable parameters, such as gait speed, motion range of joints and body balance.
D. A. Heldman, A. J. Espay, P. A. LeWitt, J. P. Giuffrida, “Clinician versus machine: reliability and responsiveness of motor endpoints in parkinson’s disease”, Parkinsonism & related disorders, vol. 20, no. 6, pp. 590-595, 2014.
K. Otte, B. Kayser, S. Mansow-Model, J. Verrel, F. Paul, A. U. Brandt, T. Schmitz- Hubsch, “Accuracy and reliability of the kinect version 2 for clinical measurement of motor function”, PloS one, vol. 11, no. 11, pp. e0166532, 2016.
O. O’Neil, C. Gatzidis, I. Swain, “A state of the art survey in the use of video games for upper limb stroke rehabilitation” in Virtual Augmented Reality and Serious Games for Healthcare 1, Springer, pp. 345-370, 2014.
H. Mousavi Hondori, M. Khademi, “A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation”, Journal of medical engineering, vol. 2014, no. 846514, 2014.
J. A. Gil-Gomez, R. Lloréns, M. Alcafiiz, C. Colomer, “Effectiveness of a wii balance board-based system (ebavir) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury”, Journal of neuroengineering and rehabilitation, vol. 8, no. 1, pp. 30, 2011.
E. D. Ofia, C. Balaguer, R. Cano de la Cuerda, S. Collado Vázquez, A. Jardon, “Effectiveness of serious games for leap motion on the functionality of the upper limb in parkinsons disease: A feasibility study”, Computational Intelligence and Neuroscience, vol. 2018, 2018.
K. Salter, N. Campbell, M. Richardson et al., “Outcome measures in stroke rehabilitation”, Evidence-Based Review of Stroke Rehabilitation. Heart and Stroke Foundation. Canadian Partnership for Stroke Recovery, 2014.
E. D. Ofia, R. Cano de la Cuerda, P. Sanchez-Herrera, C. Balaguer, A. Jardon, “A review of robotics in neurorehabilitation: Towards an automated process for upper limb”, Journal of Healthcare Engineering, vol. 2018, 2018.
Virtual Rehab, Virtual rehabilitation system.
K. Tanaka, J. Parker, G. Baradoy, D. Sheehan, J. R. Holash, L. Katz, “A comparison of exergaming interfaces for use in rehabilitation programs and research”, Loading…, vol. 6, no. 9, 2012.
J. E. Deutsch, M. Borbely, J. Filler, K. Huhn, P. Guarrera-Bowlby, “Use of a low-cost commercially available gaming console (wii) for rehabilitation of an adolescent with cerebral palsy”, Physical therapy, vol. 88, no. 10, pp. 1196-1207, 2008.
H. Sin, G. Lee, “Additional virtual reality training using xbox kinect in stroke survivors with hemiplegia”, American Journal of Physical Medicine & Rehabilitation, vol. 92, no. 10, pp. 871-880, 2013.
J. Wiemeyer, A. Kliem, “Serious games in prevention and rehabil-itationa new panacea for elderly people?”, European Review of Aging and Physical Activity, vol. 9, no. 1, pp. 41, 2011.
A. Pfister, A. M. West, S. Bronner, J. A. Noah, “Comparative abilities of microsoft kinect and vicon 3d motion capture for gait analysis”, Journal of medical engineering & technology, vol. 38, no. 5, pp. 274-280, 2014.
S. K. Jun, X. Zhou, D. K. Ramsey, V. N. Krovi, “A comparative study of human motion capture and computational analysis tools”, The 2nd International Digital Human Modeling Symposium, 2003.
A. M. d. C. Souza, M. A. Gadelha, E. A. Coutinho, S. R. d. Santos, A. Pantoja, A. Pereira, “A video-tracking based serious game for motor rehabilitation of post-stroke hand impairment”, SBC Journal on 3D Interactive Systems, vol. 3, no. 2, pp. 37-46, 2012.
Z. Luo, C. K. Lim, I. M. Chen, S. H. Yeo, “A virtual reality system for arm and hand rehabilitation”, Frontiers of Mechanical Engineering, vol. 6, no. 1, pp. 23-32, 2011.
O. Wasenmuller, D. Stricker, “Comparison of kinect v l and v2 depth images in terms of accuracy and precision”, Asian Conference on Computer Vision Workshop (ACCV workshop), 2016.
J. Van der Putten, J. Hobart, J. Freeman, A. Thompson, “Measuring change in disability after inpatient rehabilitation: comparison of the responsiveness of the barthel index and the functional independencemeasure”, Journal of Neurology Neurosurgery & Psychiatry, vol. 66, no. 4, pp. 480-484, 1999.
E. D. Ofia, A. Jardon, C. Balaguer, Y. Gao, S. Fallah, Y. Jin, C. Lekakou, “The automated box and blocks test an autonomous assessment method of gross manual dexterity in stroke rehabilitation” in Towards Autonomous Robotic Systems TAROS 2017, Cham: Springer, vol. 10454, pp. 101-114, 2017.
C. Rodriguez-de Pablo, J. C. Perry, F. I. Cavallaro, H. Zabaleta, T. Keller, “Development of computer games for assessment and training in post-stroke arm telerehabilitation”, Engineering in Medicine and Biology Society (EMBC) 2012 Annual International Conference of the IEEE, pp. 4571-4574, 2012.
V. Vallejo, P. Wyss, A. Chesham, A. V. Mitache, R. M. Muri, U. P. Mosimann, T. Nef, “Evaluation of a new serious game based multitasking assessment tool for cognition and activities of daily living: Comparison with a real cooking task”, Computers in human behavior, vol. 70, pp. 500-506, 2017.
B. Bonnechere, V. Sholukha, L. Omelina, M. Van Vooren, B. Jansen, S. V. S. Jan, “Suitability of functional evaluation embedded in serious game rehabilitation exercises to assess motor development across lifespan”, Gait & posture, vol. 57, pp. 35-39, 2017.
E. van der Meulen, M. A. Cidota, S. G. Lukosch, P. J. Bank, A. J. van der Helm, V. T. Visch, “A haptic serious augmented reality game for motor assessment of parkinson’s disease patients”, Mixed and Augmented Reality (ISMAR-Adjunct) 2016 IEEE International Symposium on, pp. 102-104, 2016.
C. Bosecker, L. Dipietro, B. Volpe, H. Igo Krebs, “Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke”, Neu-rorehabilitation and neural repair, vol. 24, no. 1, pp. 62-69, 2010.
L. Santisteban, M. Teremetz, J. P. Bleton, J. C. Baron, M. A. Maier, P. G. Lindberg, “Upper limb outcome measures used in stroke rehabilitation studies: a systematic literature review”, PloS one, vol. 11, no. 5, pp. e0154792, 2016.
J. W. Burke, M. McNeill, D. K. Charles, P. J. Morrow, J. H. Crosbie, S. M. McDonough, “Optimising engagement for stroke rehabilitation using serious games”, The Visual Computer, vol. 25, no. 12, pp. 1085-1099, 2009.
K. Sathian, L. J. Buxbaum, L. G. Cohen, J. W. Krakauer, C. E. Lang, M. Corbetta, S. M. Fitzpatrick, “Neurological principles and rehabilitation of action disorders common clinical deficits”, Neu-rorehabilitation and neural repair, vol. 25, no. 5 suppl, pp. 21S-32S, 2011.
P. W. Duncan, M. Propst, S. G. Nelson, “Reliability of the fugl-meyer assessment of sensorimotor recovery following cerebrovascular accident”, Physical therapy, vol. 63, no. 10, pp. 1606-1610, 1983.
J. Sanford, J. Moreland, L. R. Swanson, P. W. Stratford, C. Gow-land, “Reliability of the fugl-meyer assessment for testing motor performance in patients following stroke”, Physical therapy, vol. 73, no. 7, pp. 447-454, 1993.
A. Deakin, H. Hill, V. M. Pomeroy, “Rough guide to the fugl-meyer assessment: Upper limb section”, Physiotherapy, vol. 89, no. 12, pp. 751-763, 2003.
D. J. Gladstone, C. J. Danells, S. E. Black, “The fugl-meyer assessment of motor recovery after stroke: a critical review of its measurement properties”, Neurorehabilitation and neural repair, vol. 16, no. 3, pp. 232-240, 2002.
W. S. Kim, S. Cho, D. Baek, H. Bang, N. J. Paik, “Upper extremity functional evaluation by fugl-meyer assessment scoring using depth-sensing camera in hemiplegic stroke patients”, PloS one, vol. 11, no. 7, pp. e0158640, 2016.
via Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games – IEEE Conference Publication
This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson’s, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect’s accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.
Traditionally, a great portion of physical therapy and rehabilitation assessment of stroke patients is based on a therapist’s observation and judgment. The assessments methods (e.g., Fugl-Meyer et al. Assessment of Physical Performance ) rely heavily on the therapists visual assessment of how the patient is performing a standard task. This process needs a trained Physical Therapist (PT) or Occupational Therapist (OCT) to spend one on one time with the patient. Yet, the assessment can be inaccurate for several reasons one of which is the subjectivity of these behavioral and clinical assessments. Sensor and computing technology that can be used for motion capture have advanced drastically in the past few years; they have become more capable and affordable. Motion capture systems (MoCap) record human body’s kinematic data with high accuracy and reliability; analysis of MoCap data results in better clinical and behavioral assessment and more efficient therapeutic decision making accordingly.
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