An alternative approach to measure quantity and smoothness of the human limb motions; pp. 298–308Full article in PDF format | doi: 10.3176/eng.2013.4.05
The present paper is devoted to the problem of measuring and modelling the changes in human motor functions. Nowadays, overwhelming majority of techniques for motion analysis and gesture recognition are based on feature extraction, pattern recognition and clustering. An alternative approach to measure and model changes in motor functions is proposed. Unlike feature extraction or pattern recognition techniques, the proposed approach concentrates its attention on the total quantity and smoothness of the human limb movements. The latter constitutes the main distinctive feature of the proposed technique. When changes of human motor functions are caused by learning of a new motor activity, amount and smoothness of the movements may provide necessary information to measure the effectiveness of the training technique. The notion “motion mass” is introduced as a measure associated with the motion, which describes how much and how smoothly certain joints have moved. Practical example of learning the ball throwing is used to demonstrate the ability of the proposed approach to measure the changes in motor functions and distinguish their performance on different stages of the learning process
1. Holte, M. B., Cuong Tran, Trivedi, M. M. and Moeslund, T. B. Human pose estimation and activity recognition from multi-view videos: Comparative explorations of recent developments. IEEE J. Selected Topics in Signal Process., 2012, 6, 538–552.
2. Anokhin, P. K. Essays on the Physiology of Functional Systems. Medicina, Moscow, 1975 (in Russian).
3. Anokhin, P. K. Selected Works. Philosophical Aspects of the Theory of Functional Systems (Konstantinov, F. V., Lomov, B. F. and Schvyrkov, V. B., eds). Nauka, Moscow, 1978 (in Russian).
4. Toomela, A. Biological roots of foresight and mental time travel. Integr. Psych. Behav. Sci., 2010, 44, 97–125.
5. Zhao, X., Li, X., Pang, C. and Wang, S. Human action recognition based on semi-supervised discriminant analysis with global constraint. Neurocomputing, 2013, 105, 45–50.
6. Fernández-Baena, A., Susin, A. and Lligadas, X. Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments. In 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS). Bucharest, 2012, 656–661.
7. Yao-Jen Chang, Shu-Fang Chen and Jun-Da Huang. A kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities, 2011, 32, 2566–2570.
8. Yoshimitsu, K., Muragaki, Y., Maruyama, T., Yamato, M. and Iseki, H. Development and initial clinical testing of 'OPECT', an innovative device for fully intangible control of the intraoperative image displaying monitor by the surgeon. Neurosurgery, 2014. Forthcoming.
9. Takai, M. Extracting method of characteristic posture from human behavior for surveillance camera. In Proc. International Joint Conference on Instrumentation, Control and Information Technology. Fukuoka, 2009, 159–164.
10. Nõmm, S., Toomela, A. and Borushko, J. Alternative approach to model changes of human motor functions. In European Modelling Symposium EMS2013. Manchester, United Kingdom, 2013.
11. Wall, J. C., Bell, C., Campbell, S. and Davis, J. The Timed Get-up-and-Go test revisited: measurement of the component tasks. J. Rehabil. Res. Dev., 2000, 37(1), 109–114.12. Herman, T., Weiss, A., Brozgol, M., Giladi, N. and Hausdorff, J. M. Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp. Brain Res., 2013, 1–9.
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