Slicing the 3D space into planes for the fast interpretation of human motion
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(94) . power and coordination of hands. swallowing. time. time. speech. sexuality. normal skin sensation. Modified MSFC:(not EDSS!) - ambulation T25FW,6MW - upper limbs (9HPT) - cognition PASAT-3,SDMT - vision (LCSLC). motor Fatigue (FSMC) cognitive Depression (HADS) Anxiety (HADS) [Quality of life (MSIS-29)]. ⇓. ⇓. ⇓. ⇓. bowel control mood. awakefulness and alertness. 4×. 0 10 20 30 40 0 10 20 30 40 percentage of MS patients (left: <5 years, right: >15 years). 07%5>"4&0 %&AG&)(& E"% 68 86G'F E37>& 23& '&"> )G?,&' E"% sensors or markers. It avoids the use of a treadmill, allows to analyse the gait both during straight lines and turns, and to analyze long walking tests, therefore allowing for an analysis of the motor fatigability [10].. =. no change in therapy reevaluate in 6 months. 1×. =. reevaluate before 3 months. 2×. or 1×. =. consider optimization or change of therapy. ,4 (>G%2&' % %&K&'"> (4(>&% !B79%C *!=# 26 *!>##C.
(95) /. Figure 12: GAIMS measures feet trajectories with range laser scanners and derives many gait characteristics.. Figure 13: I-see-3D can project on the floor, in realtime, the feet trajectories observed with GAIMS.. Measuring the trajectories of the lower limbs extremities is sufficient, as we demonstrated that GAIMS measures an important quantity of information about the state of the patient, which is done with an adequate accuracy. In particular, GAIMS is able, with the help of machine learning techniques, to better detect ataxia than gait disorder specialists [11], and to detect when ataxia is increased between two successive visits of the same patient [12]. Moreover, it is possible to derive scores based on objective and quantitative measures of the gait that are well correlated with the subjective score used by neurologists [13]. Finally, GAIMS has been able to detect significant differences between healthy people, and different disability levels groups of people with MS [14].. References [1] S. Piérard, S. Azrour, and M. Van Droogenbroeck. Design of a reliable processing [6] M. Teixidó, T. Pallejà, M. Tresanchez, M. Norgués, and J. Palacín. Measuring pipeline for the non-intrusive measurement of feet trajectories with lasers. In IEEE oscillating walking paths with a LIDAR. Sensors, 11:5071–5086, 2011. International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages [7] S. Lejeune. Reconnaissance de personnes sur base des caractéristiques de la marche 4399–4403, Florence, Italy, May 2014. observées avec des capteurs laser. Master’s thesis, University of Liège, Belgium, 2014. [2] X. Song, X. Shao, H. Zhao, J. Cui, R. Shibasaki, and H. Zha. An online approach: [8] C. Heesen, J. Böhm, C. Reich, J. Kasper, M. Goebel, and S. Gold. Patient perception Learning-semantic-scene-by-tracking and tracking-by-learning-semantic-scene. In IEEE of bodily functions in multiple sclerosis: gait and visual function are the most valuable. International Conference on Computer Vision and Pattern Recognition (CVPR), pages Multiple Sclerosis, 14:988–991, 2008. 739–746, San Francisco, USA, June 2010. [9] M. Stangel, I. Penner, B. Kallmann, C. Lukas, and B. Kieseier. Towards the [3] M. Mucientes and W. Burgard. Multiple hypothesis tracking of clusters of people. In implementation of ’no evidence of disease activity’ in multiple sclerosis treatment: the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages multiple sclerosis decision model. Therapeutic Advances in Neurological Disorders, 692–697, Beijing, China, October 2006. 8(1):3–13, January 2015. [4] O. Barnich, S. Piérard, and M. Van Droogenbroeck. A virtual curtain for the detection [10] S. Piérard, R. Phan-Ba, and M. Van Droogenbroeck. Understanding how people with of humans and access control. In Advanced Concepts for Intelligent Vision Systems MS get tired while walking. Multiple Sclerosis Journal, 23(S11):406, September 2015. (ACIVS), Part II, pages 98–109, Sydney, Australia, December 2010. Proceedings of ECTRIMS 2015 (Barcelona, Spain), P812 (selected as a "top score [5] T. Frenken, M. Lipprandt, M. Brell, M. Gövercin, S. Wegel, E. Steinhagen-Thiessen, poster"). and A. Hein. Novel approach to unsupervised mobility assessment tests: Field trial for [11] S. Piérard, R. Phan-Ba, and M. Van Droogenbroeck. Machine learning techniques to aTUG. In 6th International Conference on Pervasive Computing Technologies for assess the performance of a gait analysis system. In European Symposium on Artificial Healthcare (PervasiveHealth), pages 131 –138, San Diego, USA, May 2012. Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 419–424, Bruges, Belgium, April 2014.. [12] S. Piérard, S. Azrour, R. Phan-Ba, and M. Van Droogenbroeck. Detection and characterization of gait modifications, for the longitudinal follow-up of patients with neurological diseases, based on the gait analyzing system GAIMS. In BIOMEDICA (the European Life Sciences Summit), Maastricht, The Netherlands, June 2014. [13] S. Azrour, S. Piérard, P. Geurts, and M. Van Droogenbroeck. Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 649–654, Bruges, Belgium, April 2014. [14] S. Piérard, S. Azrour, R. Phan-Ba, V. Delvaux, P. Maquet, and M. Van Droogenbroeck. Diagnosing multiple sclerosis with a gait measuring system, an analysis of the motor fatigue, and machine learning. Multiple Sclerosis Journal, 20(S1):171, September 2014. Proceedings of ACTRIMS/ECTRIMS 2014 (Boston, USA), P232.. Acknowledgements. We are grateful to the volunteers involved in the project GAIMS, to the Walloon region of Belgium (www.wallonie.be) for partly funding it, and to BEA (www.bea.be) for the sensors. Samir Azrour is supported by a research fellowship of the Belgian National Fund for Scientific Research (www.frs-fnrs.be).. Human Motion Analysis for Healthcare Applications — London, UK — May 19th 2016.
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