Rehabilitation Systems Based on Visualization Techniques: A Review
Toshiaki Tsuji and Kunihiro Ogata
255 Shimo-okubo, Sakura-ku, Saitama City, Saitama 338-8570, Japan
-  K. Takiyama and M. Okada, “Recovery in stroke rehabilitation through the rotation of preferred directions induced by bimanual movements: a computational study,” PloS one 7.5: e37594, 2012.
-  R. Sigrist, G. Rauter, R. Riener, and P. Wolf, “Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review,” Psychonomic bulletin & review, Vol.20, No.1, pp. 21-53, 2013.
-  A. Iriki, M. Tanakam and Y. Iwamura, “Coding of Modified Body Schema during Tool Use by Macaque Postcentral Neurons,” Neuroreport, Vol.7, pp. 2325-2330, 1996.
-  L. M. Smurr, K. Gulick, K. Yancosek, and O. Ganz, “Managing the upper extremity amputee: a protocol for success,” J. of hand therapy, Vol.21, Issue 2, pp. 160-176, 2008.
-  A. Murai, K. Kurosaki, K. Yamane, and Y. Nakamura, “Musculoskeletal-see-through mirror: Computational modeling and algorithm for whole-body muscle activity visualization in real time,” Progress in Biophysics and Molecular Biology, Vol.103, Issue 2 pp. 310-317, 2010.
-  J. B. Dingwell, B. L. Davis, and D. M. Frazier, “Use of an instrumented treadmill for real-time gait symmetry evaluation and feedback in normal and trans-tibial amputee subjects,” Prosthetics and Orthotics Int., Vol.20, No.2, pp. 101-110, 1996.
-  B. L. Davis, M. Ortolano, K. Richards, J. Redhed, J. Kuznicki, and V. Sahgal, “Realtime Visual Feedback Diminishes Energy Consumption of Amputee Subjects During Treadmill Locomotion,” AAOP, Vol.16, No.2, pp. 49-54, 2004.
-  R. Sabe, T. Hayashi, and Y. Sankai, “Visual feedback system showing loads on handrails for gait training,” 2012 IEEE/SICE Int. Symposium on System Integration (SII), pp. 337-342, 2012.
-  K. Segawa, “DIGITAL MIRROR APPARATUS,” United States Patent Application Publication, US20110210970A1, 2009.
-  V. S. Ramachandran and E. L. Altschuler, “The use of visual feedback, in particular mirror visual feedback, in restoring brain function,” Brain A J. of Neurology, Vol.132, Issue 7, pp. 1693-1710, 2009.
-  K. Sato, S. Fukumori, T. Matsusaki, T. Maruo, S. Ishikawa, H. Nishie, K. Takata, H. Mizuhara, S. Mizobuchi, H. Nakatsuka, M. Matsumi, A. Gofuku, M. Yokoyama, and K. Morita, “Nonimmersive Virtual Reality Mirror Visual Feedback Therapy and Its Application for the Treatment of Complex Regional Pain Syndrome: An Open-Label Pilot Study,” Pain Medicine, Vol.11, Issue 4, pp. 622-629, 2010.
-  L. Zhang, B. C. Abreu, G. S. Seale, B. Masel, C. H. Christiansen, and K. J. Ottenbacher, “A virtual reality environment for evaluation of a daily living skill in brain injury rehabilitation: reliability and validity,” Archives of Physical Medicine and Rehabilitation, Vol.84, Issue 8, pp. 1118-1124, 2003.
-  J. H. Lee, J. Ku, W. Cho, W. Y. Hahn, I. Y. Kim, S.-M. Lee, Y. Kang, D. Y. Kim, T. Yu, B. K. Wiederhold, M. D. Wiederhold, and S. I. Kim, “A Virtual Reality System for the Assessment and Rehabilitation of the Activities of Daily Living” CyberPsychology and Behavior, Vol.6 Issue 4, pp. 383-390, 2003.
-  B. J. Darter and J. M. Wilken, “Gait Training With Virtual Reality-Based Real-Time Feedback: Improving Gait Performance Following Transfemoral Amputation,” Physical Therapy, Vol.91, No.9, pp. 1385-1394, 2011.
-  M. Schwenk, G. S. Grewal, B. Honarvar, S. Schwenk, J. Mohler, D. S. Khalsa, and B. Najafi, “Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults.” J. of NeuroEngineering and Rehabilitation, Vol.11, Issue 164, 2013.
-  J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, “Real-Time Human Pose Recognition in Parts from Single Depth Images,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1297-1304, 2011.
-  R. Llorens, M. Alcaniz, C. Colomer, and M. D. Navarro, “Balance recovery through virtual stepping exercises using Kinect skeleton tracking: a follow-up study with chronic stroke patients,” Studies in Health Technology and Informatics, Vol.181, pp. 108-112, 2012.
-  Y.-J. Chang, W.-Y. Han, and Y.-C. Tsai, “A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy,” Research in Development Disabilities, Vol.34, Issue 11, pp. 3654-3659, 2013.
-  D. Feygin, M. Keehner, and F. Tendick, “Haptic guidance: Experimental evaluation of a haptic training method for a perceptual motor skill,” Proc. 10th Symposium on In Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 40-47, 2002.
-  M. H. Milot, L. Marchal-Crespo, C. S. Green, S. C. Cramer, and D. J. Reinkensmeyer, “Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals,” Experimental brain research, Vol.201, No.2, pp. 119-131, 2010.
-  J. L. Patton, M. E. Stoykov, M. Kovic, and F. A. Mussa-Ivaldi, “Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors,” Experimental brain research, Vol.168, No.3, pp. 368-383, 2006.
-  M. L. Aisen et al., “The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke,” Archives of neurology, Vol.54, No.4, pp. 443-446, 1997.
-  S. Okada et al., “TEM: a therapeutic exercise machine for the lower extremities of spastic patients.” Advanced Robotics, Vol.14, No.7, pp. 597-606, 2001.
-  L. Lunenburger et al., “Biofeedback in gait training with the robotic orthosis Lokomat,” Proc. 26th Annual Int. Conf. of the Engineering in Medicine and Biology Society, pp. 4888-4891, 2004.
-  N. N. Byl, G. M. Abrams et al., “Chronic stroke survivors achieve comparable outcomes following virtual task specific repetitive training guided by a wearable robotic orthosis (UL-EXO7) and actual task specific repetitive training guided by a physical therapist,” J. of Hand Therapy, Vol.26, No.4, pp. 343-352, 2013.
-  J. Furusho and M. Sakaguchi, “New actuators using ER fluid and their applications to force display devices in virtual reality and medical treatments,” Int. J. of Modern Physics B, Vol.13, No.14n16, pp. 2151-2159, 1999.
-  M. Kawato, “Feedback-error-learning neural network for supervised learning,” R. Eckmiller (Ed.), Advanced neural computers, Amsterdam: North-Holland, pp. 365-372, 1990.
-  Y. Wei, P. Bajaj, R. Scheidt, and J. Patton, “Visual error augmentation for enhancing motor learning and rehabilitative relearning,” Proc. 9th Int. Conf. on Rehabilitation Robotics, pp. 505-510, 2005.
-  F. Abdollahi, S. V. Rozario, R. V. Kenyon, J. L. Patton, E. Case, M. Kovic, and M. Listenberger, “Arm control recovery enhanced by error augmentation,” Proc. IEEE Int. Conf. on Rehabilitation Robotics (ICORR), pp. 1-6, 2011.
-  N. Shirzad and H. F. Van der Loos, “Physiological responses to error amplification in a robotic reaching adaptation task,” Proc. 36th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2318-2321, 2014.
-  C. Casellato, A. Pedrocchi, G. Zorzi, L. Vernisse, G. Ferrigno, and N. Nardocci, “EMG-based visual-haptic biofeedback: a tool to improve motor control in children with primary dystonia,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.21, No.3, pp. 474-480, 2013.
-  D. Morris, H. Tan, F. Barbagli, T. Chang, and K. Salisbury, “Haptic Feedback Enhances Force Skill Learning,” Proc. IEEE Computer Society World Haptics Conf., pp. 21-26, 2007.
-  B. R. Brewer, R. Klatzky, and Y. Matsuoka, “Effects of visual feedback distortion for the elderly and the motor-impaired in a robotic rehabilitation environment,” Proc. IEEE Int. Conf. on Robotics and Automation, Vol.2, pp. 2080-2085, 2004.
-  C. Morito, T. Shimono, N. Motoi, Y. Fujimoto, T. Tsuji, Y. Hasegawa, and S. Ishii, “Development of a haptic bilateral interface for arm self-rehabilitation,” Proc. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM), pp. 804-809, 2013.
-  C. Schonauer, T. Pintaric, H. Kaufmann, S. Jansen-Kosterink, and M. Vollenbroek-Hutten, “Chronic pain rehabilitation with a serious game using multimodal input,” Proc. Int. Conf. on Virtual Rehabilitation (ICVR), pp. 1-8, 2011.
-  K. Brutsch, T. Schuler, A. Koenig, L. Zimmerli, S. Merillat, L. Lunenburger, R. Riener, L. Jancke, and A. Meyer-Heim, “Research Influence of virtual reality soccer game on walking performance in robotic assisted gait training for children,” J. Neuroeng. Rehabil., Vol.15, No.7, pp. 1-9, 2010.
-  J. Brutovsky and D. Novak, “Low-cost motivated rehabilitation system for post-operation exercises,” IEEE Eng. Med. and Biol. Soc., pp. 6663-6666, 2006.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.