Home monitoring musculo-skeletal disorders with a single 3D sensor

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2013, Pages 521-528

  Wang, Ruizhe ^a   Medioni, Gerard ^a   Winstein, Carolee J ^b   Blanco, Cesar ^c  

^a IRI   (United States)

^b Division of Biokinesiology and Physical Therapy   (United States)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL MEASUREMENTS; HOME MONITORING SYSTEMS; MUSCULO-SKELETAL; PARKINSON'S DISEASE; QUANTITATIVE EVALUATION; SPATIOTEMPORAL INFORMATION; STANDARDIZED TESTS; TEMPORAL ALIGNMENT;

NEURODEGENERATIVE DISEASES; PATTERN RECOGNITION; SENSORS; THREE DIMENSIONAL;

MUSCULOSKELETAL SYSTEM;

EID: 84884971713     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2013.83     Document Type: Conference Paper

References (25)

1. Kinesia homeview. http://glneurotech.com/ kinesia/homeview/. 2
2. Microsoft kinect sdk. http://www.microsoft.com/ en-us/kinectforwindows/. 3
3. Motus motion monitor. http://www. motusbioengineering.com/index.htm. 2
4. Objective parkinson's disease measurement. http:// kineticsfoundation. org/. 2
5. B. R. Bloem, Y. A. Grimbergen, M. Cramer, M. Willemsen, and A. H. Zwinderman. Prospective assessment of falls in parkinson's disease. Journal of neurology, 248(11):950-958, 2001. 2, 6
6. B. R. Bloem, J. M. Hausdorff, J. E. Visser, and N. Giladi. Falls and freezing of gait in parkinson's disease: a review of two interconnected, episodic phenomena. Movement Disorders, 19(8):871-884, 2004. 6
7. C.-W. Cho, W.-H. Chao, S.-H. Lin, and Y.-Y. Chen. A vision-based analysis system for gait recognition in patients with parkinsons disease. Expert Systems with applications, 36(3):7033-7039, 2009. 3
8. S. Fahn, R. Elton, U. D. Committee, et al. Unified parkinsons disease rating scale. Recent developments in Parkinsons disease, 2:153-163, 1987. 1
9. P. J. Garcia Ruiz and V. Sanchez Bernardos. Evaluation of actitracR (ambulatory activity monitor) in parkinson's disease. Journal of the neurological sciences, 270(1):67-69, 2008. 2
10. D. Gong and G. Medioni. Dynamic manifold warping for view invariant action recognition. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 571-578. IEEE, 2011. 4
11. J. Jankovic. Parkinsons disease: clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4):368-376, 2008. 6
12. W. Li, Z. Zhang, and Z. Liu. Action recognition based on a bag of 3d points. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pages 9-14. IEEE, 2010. 6
13. P. Mordohai and G. Medioni. Dimensionality estimation, manifold learning and function approximation using tensor voting. The Journal of Machine Learning Research, 11:411-450, 2010. 5
14. M. Muller and T. Roder. Motion templates for automatic classification and retrieval of motion capture data. In Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation, pages 137-146. Eurographics Association, 2006. 6
15. C. Rao, A. Gritai, M. Shah, and T. Syeda-Mahmood. Viewinvariant alignment and matching of video sequences. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 939-945. IEEE, 2003. 4
16. A. Salarian, H. Russmann, F. J. Vingerhoets, P. R. Burkhard, and K. Aminian. Ambulatory monitoring of physical activities in patients with parkinson's disease. Biomedical Engineering, IEEE Transactions on, 54(12):2296-2299, 2007. 3
17. A. Salarian, H. Russmann, C. Wider, P. R. Burkhard, F. J. Vingerhoets, and K. Aminian. Quantification of tremor and bradykinesia in parkinson's disease using a novel ambulatory monitoring system. Biomedical Engineering, IEEE Transactions on, 54(2):313-322, 2007. 3
18. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1297-1304. IEEE, 2011. 3
19. B. W. Silverman. Density estimation for statistics and data analysis, volume 26. Chapman & Hall/CRC, 1986. 7
20. B. van Hilten, J. I. Hoff, H. A. Middelkoop, E. A. van der Velde, G. A. Kerkhof, A. Wauquier, H. A. Kamphuisen, and R. A. Roos. Sleep disruption in parkinson's disease: assessment by continuous activity monitoring. Archives of neurology, 51(9):922, 1994. 2
21. J.Wang, Z. Liu, Y.Wu, and J. Yuan. Mining actionlet ensemble for action recognition with depth cameras. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 1290-1297. IEEE, 2012. 6
22. A. Weiss, S. Sharifi, M. Plotnik, J. P. van Vugt, N. Giladi, and J. M. Hausdorff. Toward automated, at-home assessment of mobility among patients with parkinson disease, using a body-worn accelerometer. Neurorehabilitation and Neural Repair, 25(9):810-818, 2011. 2
23. L. Xia, C.-C. Chen, and J. Aggarwal. View invariant human action recognition using histograms of 3d joints. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 20-27. IEEE, 2012. 6
24. F. Zhou and F. De la Torre. Canonical time warping for alignment of human behavior. Advances in Neural Information Processing Systems (NIPS), pages 1-9, 2009. 4
25. W. Zijlstra and A. L. Hof. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & posture, 18(2):1-10, 2003. 2