Close range depth sensing cameras for virtual reality based hand rehabilitation

Darryl Charles (Senior Lecturer in Computing, based at Computer Science Research Institute, University of Ulster, Coleraine, Northern Ireland)
Katy Pedlow (Research Associate, based at Centre for Health and Rehabilitation Technologies, University of Ulster, Jordanstown, Northern Ireland)
Suzanne McDonough (Professor of Health and Rehabilitation, based at Centre for Health and Rehabilitation Technologies, University of Ulster, Jordanstown, Northern Ireland)
Ka Shek (Software Engineer, based at SilverFish Studios, Coleraine, Northern Ireland)
Therese Charles (CEO, based at SilverFish Studios, Coleraine, Northern Ireland)

Journal of Assistive Technologies

ISSN: 1754-9450

Publication date: 9 September 2014



The Leap Motion represents a new generation of depth sensing cameras designed for close range tracking of hands and fingers, operating with minimal latency and high spatial precision (0.01 mm). The purpose of this paper is to develop virtual reality (VR) simulations of three well-known hand-based rehabilitation tasks using a commercial game engine and utilising a Leap camera as the primary mode of interaction. The authors present results from an initial evaluation by professional clinicians of these VR simulations for use in their hand and finger physical therapy practice.


A cross-disciplinary team of researchers collaborated with a local software company to create three dimension interactive simulations of three hand focused rehabilitation tasks: Cotton Balls, Stacking Blocks, and the Nine Hole Peg Test. These simulations were presented to a group of eight physiotherapists and occupational therapists (n=8) based in the Regional Acquired Brain Injury Unit, Belfast Health, and Social Care Trust for evaluation. After induction, the clinicians attempted the tasks presented and provided feedback by filling out a questionnaire.


Results from questionnaires (using a Likert scale 1-7, where 1 was the most favourable response) revealed a positive response to the simulations with an overall mean score across all questions equal to 2.59. Clinicians indicated that the system contained tasks that were easy to understand (mean score 1.88), and though it took several attempts to become competent, they predicted that they would improve with practice (mean score 2.25). In general, clinicians thought the prototypes provided a good illustration of the tasks required in their practice (mean score 2.38) and that patients would likely be motivated to use the system (mean score 2.38), especially young patients (mean score 1.63), and in the home environment (mean score 2.5).


Cameras offer an unobtrusive and low maintenance approach to tracking user motion in VR therapy in comparison to methods based on wearable technologies. This paper presents positive results from an evaluation of the new Leap Motion camera for input control of VR simulations or games. This mode of interaction provides a low cost, easy to use, high-resolution system for tracking fingers and hands, and has great potential for home-based physical therapies, particularly for young people.



Charles, D., Pedlow, K., McDonough, S., Shek, K. and Charles, T. (2014), "Close range depth sensing cameras for virtual reality based hand rehabilitation", Journal of Assistive Technologies, Vol. 8 No. 3, pp. 138-149.

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