Main Article Content
In this paper, a low-cost markerless system based on RGB-D sensor was developed for rehabilitation purposes.
The purpose of this system is to provide the physical therapist with the adequate data to characterize the arm motion in post-stroke rehabilitation, athletic injuries recovery or other possible human arm motion recording and analysis issues. The proposed system uses the Microsoft Kinect sensor to determine the motion of the patient arm.
The 3D position of the joints (shoulder, elbow and wrist) and the centers of mass of arm segments (upper and forearm) are estimated during the motion. The system then calculates the velocities and energies expenditure by each segment of the arm.
More data are estimated by the system such as the angles of each joint (shoulder and elbow) and the angular velocities of the arm segments, where these data are useful to help the therapist with a full overview in the analysis about the patient motion.
With the help of these data, the therapist has the analysis of the arm, and comparing with the previous analysis of the patient arm motion, a relative performance can be estimated, allowing a quantitative evaluation of the recovery of the patient over series of exercises.
Furthermore, as a part of the work, 3D Virtual Reality System $(VRS)$ was developed. This 3D VRS can be a beneficial environment for learning a motor task and it is important in terms of the motion motivation, since it presented to the patient as a rewarded computer game.
Upon receipt of accepted manuscripts, authors will be invited to complete a copyright license to publish the paper. At least the corresponding author must send the copyright form signed for publication. It is a condition of publication that authors grant an exclusive licence to the the INFOCOMP Journal of Computer Science. This ensures that requests from third parties to reproduce articles are handled efficiently and consistently and will also allow the article to be as widely disseminated as possible. In assigning the copyright license, authors may use their own material in other publications and ensure that the INFOCOMP Journal of Computer Science is acknowledged as the original publication place.
A. K. Agnihotri, B. Purwar, N. Jeebun, and S.
Agnihotri. Determination of sex by hand dimensions.
The Internet Journal of Forensic Science,
Attygalle, S., Duff, M., Rikakis, T., and He, J.
Low-cost, at-home assessment system with wii remote
based motion capture. In Virtual Rehabilitation,
, pages 168–174. IEEE, 2008.
Balasubramanian, S.,Wei, R., Perez, M., Shepard,
B., Koeneman, J., Koeneman, E., and He, J. Rupert:
An exoskeleton robot for assisting rehabilitation
of arm functions. In Virtual Rehabilitation,
, pages 163–167. IEEE, 2008.
Cameirão, M. S., Oller, E. D., Verschure, P. F.,
et al. Using a multi-task adaptive vr system for upper
limb rehabilitation in the acute phase of stroke.
In Virtual Rehabilitation, 2008, pages 2–7. IEEE,
Chan, M., Giddings, D., Chandler, C., Craggs, C.,
Plant, R., and Day, M. An experimentally confirmed
statistical model on arm movement. Human
movement science, 22(6):631–648, 2004.
Creighton, R. H. Unity 3D game development by
example: beginner’s guide. Packt Publ., Birmingham
Dellon, B. and Matsuoka, Y. Feedback distortion
to augment controllability of human limb motion.
In Virtual Rehabilitation, 2008, pages 22–27.
DP. Inman, J. Peaks, K. Loge, and V. Chen. Teaching
orthopedically impaired children to drive motorized
wheelchairs in virtual reality. In Center on
Disabilities Virtual Reality Conference. 1994.
Gams, A. and Lenar˘ci˘c, J. Humanoid arm kinematic
modeling and trajectory generation. In
Biomedical Robotics and Biomechatronics, 2006.
BioRob 2006. The First IEEE/RAS-EMBS International
Conference on, pages 301–305. IEEE,
Hans-Martin Schmidt and Ulrich Lanz. Surgical
Anatomy of the Hand. 1 edition, 2003.
Kathryn LaBelle. Evaluation of Kinect Joint
Tracking for Clinical and in-home Stroke Rehabilitation
Tools. PhD thesis, Notre Dame, Indiana,
Lanfermann, G., te Vrugt, J., Timmermans, A.,
Bongers, E., Lambert, N., and van Acht, V. Philips
stroke rehabilitation exerciser. Technical Aids for
Lenar˘ci˘c, J. and Staniši´c, M. A humanoid shoulder
complex and the humeral pointing kinematics.
Robotics and Automation, IEEE Transactions on,
Microsoft Corporation. Kinect xbox
Microsoft Research. Kinect for Windows SDK
Beta: Programming Guide. Microsoft Co., beta
Nigel Foreman, Paul Wilson, and Danae Stanton.
Vr and spatial awareness in disabled children.
Communications of the ACM, 40(8):76–77, 1997.
Popescu, V. G., Burdea, G. C., Bouzit, M., and
Hentz, V. R. A virtual-reality-based telerehabilitation
system with force feedback. Information
Technology in Biomedicine, IEEE Transactions
on, 4(1):45–51, 2000.
Prange, G., Krabben, T., Molier, B., van der Kooij,
H., and Jannink, M. A low-tech virtual reality
application for training of upper extremity motor
function in neurorehabilitation. In Virtual Rehabilitation,
, pages 8–12. IEEE, 2008.
Schönauer, C., Pintaric, T., Kaufmann, H.,
Jansen-Kosterink, S., and Vollenbroek-Hutten, M.
Chronic pain rehabilitation with a serious game
using multimodal input. In Virtual Rehabilitation
(ICVR), 2011 International Conference on, pages
V. Zatsiorsky and V. Seluyanov. The mass and
inertia characteristics of the main segments of the
human body. Biomechanics VIII-B, 56(2):1152–
V. Zatsiorsky and V. Seluyanov. Estimation of the
mass and inertia characteristics of the human body by means of the best predictive regression equations.
Biomechanics IX-B, pages 233–239, 1985.
Vladimir M. Zatsiorsky. Kinematics of Human
Motion. ilustrada edition, 1998.
Webb, J. and Ashley, J. Beginning Kinect Programming
with the Microsoft Kinect SDK. Apress,
White, D., Burdick, K., Fulk, G., Searleman,
J., and Carroll, J. A virtual reality application
for stroke patient rehabilitation. In Mechatronics
and Automation, 2005 IEEE International Conference,
volume 2, pages 1081–1086. IEEE.
Wilson, P. H., Duckworth, J., Mumford, N., Eldridge,
R., Guglielmetti, M., Thomas, P., Shum,
D., and Rudolph, H. A virtual tabletop workspace
for the assessment of upper limb function in Traumatic
Brain Injury (TBI). In Virtual Rehabilitation,
, pages 14–19. IEEE, 2007.