Abstract
This research aims at developing a method to detect abnormal gait from depth images and to classify abnormal gaits of patients. Recently, motion capture system is popular used in the analysis of human gaits. However, a motion capture system remains many weaknesses such as costly and complicated set up, and requiring professional technicians to manage the motion capture system. This work introduces a new approach to detect and classify abnormal gaits by using depth images and skeleton joints of the human subjects detected from the images. The system feeds the data including depth images and positions in 3D of skeleton joints into a hidden Markov model as well as K-means clustering to approach a new effective solution to replace conventional motion capture system. We tested our approach with a large number of subjects to validate its performance and shown that the proposed our system performs well. Therefore, this system may be applicable to help doctors in medical diagnosis and treatment process.
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References
Martin MP, O’Neill D (2004) Vascular higher-level gait disorders—a step in the right direction? Lancet 363(9402):8
Tatacipta D, Andi IM, Sandro M. Development of affordable of optical based gait analysis systems
Sanders RD et al (2010) Gait and its assessment in psychiatry. Psychiatry (Edgmont) 7(7):38–43
Chaaraoui A, Padilla-Lopez J, Florez-Revuelta F (n.d.) Abnormal gait detection with RGB-D devices using joint motion history features. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG)
Nguyen TN, Huynh HH, Jean M (2015) Abnormal gait detection with one camera using hidden Markov model
Prochazka A, Vysata O, Valis M, Yadollahi M (2015) The MS Kinect use for 3D modeling and gait analysis in the Matlab environment: 1
Shotton J, Girshick R, Fitzgibbon A, Sharp T, Cook M, Finocchio M, Blake A. (n.d.). Efficient human pose estimation from single depth images. Decis Forests Comput Vis Med Image Anal: 175–192
Khorasani A, Daliri M (2014) HMM for classification of Parkinson’s disease based on the raw gait data. J Med Syst
Taborri J, Scalona E, Rossi S, Palermo E, Patane F, Cappa P. Real-time gait detection based on hidden Markov model: is it possible to avoid training procedure? In: 2015 IEEE international symposium on medical measurements and applications (MeMeA) proceedings
Yang H, Park A, Lee S (n.d.) Human-robot interaction by whole body gesture spotting and recognition. In: 18th international conference on pattern recognition (ICPR’06)
Chen M, Huang B, Xu Y (n.d.) Human abnormal gait modeling via hidden markov model. In: 2007 international conference on information acquisition
Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Blake A. Real-time human pose recognition in parts from single depth images. Mach Learn Comput Vision Stud Comput Intell: 119–135
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. University of California Press, pp 281–297
Norris JR (1998) Markov chains. Cambridge University Press
Acknowledgements
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2013.11.
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Tuan, N.V.A., Vo Van, T., Hau, N.V.D., Thang, N.D. (2018). Abnormal Gait Detection and Classification Using Depth Camera. In: Vo Van, T., Nguyen Le, T., Nguyen Duc, T. (eds) 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) . BME 2017. IFMBE Proceedings, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-10-4361-1_128
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DOI: https://doi.org/10.1007/978-981-10-4361-1_128
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