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Reseach Article

Distance Physical Rehabilitation System Framework with Multi-Kinect Motion Captured Data

by Mohammad Rafiuzzaman, Cemil Oz
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2015
Authors: Mohammad Rafiuzzaman, Cemil Oz
10.5120/cae-1558

Mohammad Rafiuzzaman, Cemil Oz . Distance Physical Rehabilitation System Framework with Multi-Kinect Motion Captured Data. Communications on Applied Electronics. 1, 5 ( April 2015), 29-39. DOI=10.5120/cae-1558

@article{ 10.5120/cae-1558,
author = { Mohammad Rafiuzzaman, Cemil Oz },
title = { Distance Physical Rehabilitation System Framework with Multi-Kinect Motion Captured Data },
journal = { Communications on Applied Electronics },
issue_date = { April 2015 },
volume = { 1 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 2394-4714 },
pages = { 29-39 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume1/number5/332-1558/ },
doi = { 10.5120/cae-1558 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T18:37:40.465395+05:30
%A Mohammad Rafiuzzaman
%A Cemil Oz
%T Distance Physical Rehabilitation System Framework with Multi-Kinect Motion Captured Data
%J Communications on Applied Electronics
%@ 2394-4714
%V 1
%N 5
%P 29-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visiting physical therapists to the clinics for physical rehabilitation in regular basis is a very long and time-consuming trip where the final result for success is truly hard to see in daily training. That's why technological development in traditional physical rehabilitation system is both important, interesting and its effects on patients' time-management process is huge. In this paper we have proposed a framework for distance physical rehabilitation system using motion captured data from multiple Kinects which can interact directly with the patients, even grasp and track their movements so as to send those data back to the doctors in clinics using Windows Azure. Its goal is to coach patients through their physical therapy exercises and make those exercises a more enjoyable experience and bring physical therapy alive for them at their homes, the same way doctors interact with them in clinics.

References
  1. L. Kovar, M. Gleicher, and F. H. Pighin. Motion graphs. ACM Trans. Graph. , 21(3):473–482, 2002.
  2. Lindsay I Smith. A tutorial on Principal Components Analysis. February 26, 2002.
  3. Hubert P. H. Shum, Edmond S. L. Ho. Real-time Physical Modelling of Character Movements with Microsoft Kinect. VRST'12, December 10–12, 2012 ACM 978-1-4503-1469-5/12/12.
  4. Microsoft Corporation. Kinect for windows SDK programming guide version 1. 5. 2012.
  5. H. J. Luinge and P. H. Veltink. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Medical and Biological Engineering and Computing, 43(2):273–282, 2005.
  6. C. -C. Yang and Y. -L. Hsu. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8):7772–7788, 2010.
  7. StepanObdrzalek, GregorijKurillo, FerdaOfli, RuzenaBajcsy, Edmund Seto, Holly Jimison and Michael Pavel. Accuracy and Robustness of Kinect Pose Estimation in the Context of Coaching of Elderly Population. National Science Foundation (NSF) grant 1111965 and by Grant Number HHS 90TR0003/01, 2012.
  8. Moshe Gabel, Ran Gilad-Bachrach, Erin Renshaw and Assaf Schuster. Full Body Gait Analysis with Kinect. The Department of Computer Science, Technion – Israel Institute of Technology.
  9. Brian M. Williamson and Dr. Joseph J. LaViola Jr. Multi-Kinect Tracking for Dismounted Soldier Training. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2012.
  10. SB Pollard, JP Frisby. Transparency and the uniqueness constraint in human and computer stereo vision. Nature 347, 553 - 556 (11 October 1990); doi:10. 1038/347553a0
  11. ChanjiraSinthanayothin, NonlapasWongwaen, WisarutBholsithi, Skeleton Tracking using Kinect Sensor & Displaying in 3D Virtual Scene, International Journal of Advancements in Computing Technology(IJACT) Volume4, Number11, June 2012.
  12. Chien-Yen Chang, Belinda Lange, Mi Zhang, Sebastian Koenig, Phil Requejo, NoomSomboon, Alexander A. Sawchuk, and Albert A. Rizzo. Towards Pervasive Physical Rehabilitation Using Microsoft Kinect. 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.
  13. National Spinal Cord Injury Statistical Center. Spinalcord injury facts and figures at a glance. Birmingham, Alabama, February 2011.
  14. K. J. O'Donovan, B. R. Greene, D. McGrath, R. O'Neill, A. Burns, and B. Caulfield. SHIMMER: A new tool for temporal gait analysis. In EMBC, pages 3826–3829, 2009.
  15. Eli Maor. The Pythagorean Theorem: a 4,000-year history. 2007 - books. google. com
  16. J. J. Kavanagh and H. B. Menz. Accelerometry: a technique for quantifying movement patterns during walking. Gait & Posture, 28(1):1–15, 2008.
  17. DA Butler, S Izadi, O Hilliges, D Molyneaux, S. Hodges, D. Kim. Shake'n'sense: reducing interference for overlapping structured light depth cameras. CHI '12 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Pages 1933-1936. ACM New York, NY, USA ©2012. ISBN: 978-1-4503-1015-4 doi>10. 1145/2207676. 2208335
  18. Microsoft, "Kinect," 2010. URL: http://www. xbox. com/en-us/kinect (accessed March 7, 2012).
  19. K. Khoshelham and S. O. Elberink, "Accuracy and resolution of kinect depth data for indoor mapping applications," Sensors, vol. 12, no. 2, pp. 1437–1454, 2012.
  20. Denis, L. Tupin, F. ; Darbon, J. ; Sigelle, M. Joint Filtering of SAR Interferometric and Amplitude Data in Urban Areas by TV Minimization. Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International (Volume: 5). 7-11 July 2008
  21. Blogs. technet. com. 2010-02-01. "Windows Azure General Availability - The Official Microsoft Blog - Site Home - TechNet Blogs" Retrieved 28-05-2013.
  22. Carlos F. Crispim-Junior, Baptiste Fosty, Rim Romdhane, Qiao Ma, Francois Bremond, Monique Thonnat. Combining Multiple Sensors for Event Recognition of Older People. MIIRH'13, October 22, 2013, Barcelona, Spain.
  23. Jie Li ; Humphrey, M. ; References. eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform. Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on. 19-23 April 2010
  24. Reinhard Koch, BogumilBartczak, AnatolFrick, BogumilBartczak, AnatolFrick, FalkoKellner and Ingo Schiller. Time-of-Flight-Range and Color Camera Systems for 3D-TV and Augmented Reality Applications. Institute of Computer Science Christian-Albrechts-University of Kie.
  25. Chappell, David. Introducing Windows Azure. Microsoft. October 2008.
  26. Prajakta S. Kalekar. Time series forecasting using Holt-Winters Exponential Smoothing. www. it. iitb. ac. in/~praj/acads/seminar/04329008_ExponentialSmoothing. pdf December 6, 2004.
  27. Prof. Dr. W. Toporowski, Institutfür Marketing & Handel Abteilung Handel. Smoothing methods.
Index Terms

Computer Science
Information Sciences

Keywords

Distance rehabilitation Multiple Kinects posture recognition posture database PCA skeleton tracking Windows Azure