CONCEPTUAL DESIGN AND IMPLEMENTATION FOR VISUAL TRACKING ANKLE REHABILITATION SYSTEM

Lim Chee Chin; Shafriza Nisha Basah; Sazali Yaacob; Yeap Ewe Juan

doi:10.15282/jmes.7.2014.20.0118

Authors

Lim Chee Chin School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
Shafriza Nisha Basah School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
Sazali Yaacob School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
Yeap Ewe Juan Consultant Orthopaedic Surgeon, Sime Darby Medical Centre Park City Sdn Bhd, Desa Park City, 52200 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.15282/jmes.7.2014.20.0118

Keywords:

: Visual tracking; ankle rehabilitation system (ARS); structured light camera (SLC); Red-Blue-Green-Depth (RBG-D) images.

Abstract

The simple needs of an ankle rehabilitation system are valid for medical evaluation, user-friendly, and perform efficiently at low cost. However, most of the current ankle rehabilitation systems face a lot of problems, such as inconvenient face-to-face therapy, manual evaluation by the physiotherapist, the limited number of physiotherapists, and the high cost. Therefore, the key conceptual issues in designing and implementing an ankle rehabilitation system are identified and discussed in this article in order to overcome these problems. The aim of designing an ankle rehabilitation system is to furnish an alternative for ankle sprain patients so that they can efficiently perform rehabilitation exercises in their household surroundings. Additionally, the output data from the ankle rehabilitation system provides valuable patient information for further medical evaluation and monitoring. This article describes the conceptual design phase of an ankle rehabilitation system. It starts with a needs analysis and focuses on conceptual design. Six concept options are designed based on the needs identified. The selected concept is decided based on the system needs and characteristics of the conventional ankle rehabilitation method. Finally, the preliminary implementation result is included to demonstrate the feasibility of the selected concept for the ankle rehabilitation system.

References

Lamb SE, Marsh JL, Hutton JL, Nakash R, Cooke MW. Mechanical supports for acute, severe ankle sprain: a pragmatic, multicentre, randomised controlled trial. The Lancet. 2009;373:575-81.

AAOS (American Academy of Orthopaedic Surgeons). Sprained ankle. American Orthopaedic Foot and Ankle Society; 2012.

University Health Network. Ankle rehabilitation protocol. Toronto Western Hospital; 2013.

Ulrich KT, Eppinger SD. Product design and development. 5 ed: McGraw-Hill New York; 2007.

iPiSoft Wiki. User guide for dual depth sensor configuration. 2013.

Miyahara M, Yoshida Y. Mathematical Transform Of (R, G, B) Color Data To Munsell (H, V, C) Color Data. Proc SPIE 1001, Visual Communications and Image Processing '88: Third in a Series. 3 ed. Cambridge, MA1988. p. 650-7.

Aggarwal JK, Cai Q, Liao W, Sabata B. Articulated and elastic non-rigid motion: a review. Proceedings of the 1994 IEEE Workshop on Motion of Non- Rigid and Articulated Objects, 1994, . Austin, TX1994. p. 2-14.

Hossny M, Filippidis D, Abdelrahman W, Zhou H, Fielding M, Mullins J, et al. Low cost multimodal facial recognition via kinect sensors. LWC 2012: Potent land force for a joint maritime strategy: Proceedings of the 2012 Land Warfare Conference. Australia: Commonwealth of Australia; 2012. p. 77-86.

Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, et al. Real- time human pose recognition in parts from single depth images. Commun ACM. 2013;56:116-24.

Wiredchop. Kinect biomechanics, engineering sport.

Henry P, Krainin M, Herbst E, Ren X, Fox D. RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments. In: Khatib O, Kumar V, Sukhatme G, editors. Experimental Robotics: Springer Berlin Heidelberg; 2014. p. 477-91.

Hg RI, Jasek P, Rofidal C, Nasrollahi K, Moeslund TB, Tranchet G. An RGB-D Database Using Microsoft's Kinect for Windows for Face Detection. 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS),. 8 ed2012. p. 42-6.

Gang P, Shi H, Zhaohui W, Yueming W. 3D Face Recognition using Mapped Depth Images. IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2005 CVPR Workshops. San Diego, CA, USA2005. p. 175.

Maimone A, Fuchs H. Reducing interference between multiple structured light depth sensors using motion. Virtual Reality Short Papers and Posters (VRW), 2012 IEEE. University of North Carolina at Chapel Hill2012. p. 51-4.

Butler DA, Izadi S, Hilliges O, Molyneaux D, Hodges S, Kim D. Shake'n'sense: reducing interference for overlapping structured light depth cameras. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, USA: ACM; 2012. p. 1933-6.

Khoshelham K. Accuracy analysis of kinect depth data. ISPRS workshop laser scanning. University of Twente, Netherlands2011. p. W12.

Khoshelham K, Elberink SO. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications. Sensors. 2012;12:1437-54.