A virtual reality system for arm and hand rehabilitation

Abstract

This paper presents a virtual reality (VR) system for upper limb rehabilitation. The system incorporates two motion track components, the Arm Suit and the Smart Glove which are composed of a range of the optical linear encoders (OLE) and the inertial measurement units (IMU), and two interactive practice applications designed for driving users to perform the required functional and non-functional motor recovery tasks. We describe the technique details about the two motion track components and the rational to design two practice applications. The experiment results show that, compared with the marker-based tracking system, the Arm Suit can accurately track the elbow and wrist positions. The repeatability of the Smart Glove on measuring the five fingers’ movement can be satisfied. Given the low cost, high accuracy and easy installation, the system thus promises to be a valuable complement to conventional therapeutic programs offered in rehabilitation clinics and at home.

Key words： motion tracking; rehabilitation; optical linear encoder (OLE); virtual reality

Acknowledgments

This work was supported in part by the Agency for Science, Technology and Research, Singapore, under SERC Grant 0521180050, and Media Development Authority, Singapore under NRF IDM004-005 Grant.

References

1 Taub E, Miller N E, Novack T A, Cook E W 3rd, Fleming W C, Nepomuceno C S, Connell J S, Crago J E. Technique to improve chronic motor deficit after stroke. Archives of Physical Medicine and Rehabilitation, 1993, 74(4): 347–354

PMID

2 Broeks J G, Lankhorst G J, Rumping K, Prevo A J. The long-term outcome of arm function after stroke: results of a follow-up study. Disability and Rehabilitation, 1999, 21(8): 357–364

PMID

3 Cirstea M C, Levin M F. Compensatory strategies for reaching in stroke. Brain, 2000, 123(5): 940–953

PMID

4 Rohrer B, Fasoli S, Krebs H I, Hughes R, Volpe B, Frontera W R, Stein J, Hogan N. Movement smoothness changes during stroke recovery. Journal of Neuroscience, 2002, 22(18): 8297–8304

PMID

5 Dickstein R, Heffes Y, Laufer Y, Abulaffio N, Shabtai E L. Repetitive practice of a single joint movement for enhancing elbow function in hemiparetic patients. Perceptual and Motor Skills, 1997, 85(3 Pt 1): 771–785

PMID

6 Glantz K, Rizzo A A, Graap K. Virtual reality for psychotherapy: Current reality and future possibilities. Psychotherapy (Chicago, Ill.), 2003, 40(1-2): 55–67

7 Popescu V G, Burdea G C, Bouzit M, Hentz V R. A virtual-reality-based telerehabilitation system with force feedback. IEEE Transactions on Information Technology in Biomedicine, 2000, 4(1): 45–51

PMID

8 Chen Y L. Application of tilt sensors in human-computer mouse interface for people with disabilities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2001, 9(3): 289–294

PMID

9 Tao Y, Hu H, Zhou H. Integration of vision and inertial sensors for 3d arm motion tracking in home-based rehabilitation. International Journal of Robotics Research, 2007, 26(6): 607–624

10 Burdea G, Coiffet P. Virtual reality technology.New York: Wiley, 1994

11 Kong K, Tomizuka M. Control of exoskeletons inspired by fictitious gain in human model. IEEE/ASME Transactions on Mechatronics, 2009, 14(6): 689–698

12 Zhu R, Zhou Z. A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2004, 12(2): 295–302

PMID

13 Lim K Y, Goh Y K, Dong W, Nguyen K D, Chen I M, Yeo S H, Duh H B L, Kim C G. A wearable, self-calibrating, wireless sensor network for body motion processing. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2008, 1017–1022

14 Yu H L, Chase R A, Strauch B. Atlas of hand anatomy and clinical implications. Mosby, 2004

15 Dipietro L, Sabatini A M, Dario P. Evaluation of an instrumented glove for hand-movement acquisition. Journal of Rehabilitation Research and Development, 2003, 40(2): 181–189

PMID