Stroke rehabilitation with a sensing surface

ABSTRACT

This paper presents a new sensing and interaction environment for post-stroke and upper extremity limb rehabilitation. The device is a combination of camera-based multitouch sensing and a supporting therapeutic software application that advances the treatment, provides feedback, and records a user's progress. The image-based analysis of hand position provided by a Microsoft Surface is used as an input into a tabletop game environment. Tailored image analysis algorithms assess rehabilitative hand movements. Visual feedback is provided in a game context. Experiments were conducted in a sub-acute rehabilitation center. Preliminary user studies with a stroke-afflicted population determined essential design criteria. Hand and wrist sensing, as well as the goals of the supporting game environment, engage therapeutic flexion and extension as defined by consulted physicians. Participants valued personalization of the activity, novelty, reward and the ability to work at their own pace in an otherwise repetitive therapeutic task. A "character" - game element personifying the participant's movement - was uniquely motivating relative to the media available in the typical therapeutic routine.

References

1. Adamovich, S. V. et al (2011). A virtual reality-based exercise system for hand rehabilitation post-stroke. Prescence: Teleop. Virtual Environ. 14, 2, 161--174.
2. Alankus, G. et al. (2010). Stroke therapy through motionbased games: a case study. In ACM SIGACCESS conference on Computers and accessibility. Pp. 219--226.
3. Back-y-Rita et al. (2002) Computer-assisted motivating rehabilitation for institutional, home, and educational late stroke programs. Topics Stroke Rehab. 8, 4, 1--10.
4. Chedoke Arm and Hand Activity Inventory (2004).
5. Chen, C. C. and Bode, R. K. (2011). Factors influencing therapists' decision-making in the acceptance of new technology devices in stroke rehabilitation. Am J Phys Med Rehabil. 90(5):415--425.
6. Hecht, D., Reiner, M., and Karni, A. (2008). Multisensory enhancement: gains in choice and in simple response times. Exp Brain Res , 189(2):133--143.
7. Internet Stroke Center (2012).
8. Krebs, H. I. et al. (2008). A comparison of functional and impairment-based robotic training in severe to moderate chronic stroke: a pilot study. NeuroRehabilitation, 23(1):81--87.
9. Lewis, G. N., Woods, C., Rosie, J. A. and McPherson, K. M. (2011). Virtual reality games for rehabilitation of people with stroke: Perspectives from the users. Disabil Rehabil Assist Technol 6(5):453--463.
10. Molholm, S. et al. (2004). Multisensory visual-auditory object recognition in humans: a highdensity electrical mapping study. Cereb Cortex, 14(4):452--465.
11. National Stroke Association (2006). New survey emphasizes need for more, better care after stroke.
12. Pizzamiglio, L. et al. (2005). Separate neural systems for processing action- or non-action-related sounds. Neuroimage, 24(3):852--861.
13. Stein, J. (2009). e100 neurorobotic system. Expert Rev Med Devices, 6(1):15--19.
14. Sugar, T. G. et al. (2007) Design and control of RUPERT: a device for robotic upper extremity repetitive therapy, IEEE Transactions on neural systems and rehabilitation engineering, 15(3):336--46.
15. Teder-sälejärvi, W. A., Di Russo, F., McDonald, J. J., and Hillyard, S. A. (2005). Effects of spatial congruity on audio-visual multimodal integration. J Cogn Neurosci, 17(9):1396--14.
16. World Health Organization {WHO}. (2002). Innovative Care for Chronic Conditions: Building Blocks For Action. Retrieved from http://www.who.int/chp/knowledge/publications/icccreport/en/

Index Terms

• Human-centered computing