Abstract
Virtual reality technology shows serious potential in many fields, such as cinemtic entertainment, professional training, Healthcare and clinical therapies, etc. In this paper, we propose a novel human balance capability evaluation method, which is based on crossing bridge virtual scene and video analysis. We have sampled the crossing bridge movement video of two groups of volunteers with balance ability differences, and then we proposed a balance ability classification algorithm via barycentric shifts model statistical analysis. The small sample experiment shows that our method can accurately identify the possible candidates with balance ability abnormality.
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Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under grant Nos. 61472204, 6150238.
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Jin, H., Lin, W., Xiao, Z., Liu, H., Wang, B., Li, X. (2019). Barycentric Shift Model Based VR Application for Detection and Classification on Body Balance Disorders. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_1
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DOI: https://doi.org/10.1007/978-3-030-23712-7_1
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