Segmentation and Evaluation of Continuous Rehabilitation Exercises

doi:10.16183/j.cnki.jsjtu.2021.458

Abstract

Abstract:

To provide an accurate and objective feedback on rehabilitation training movements and to improve the motivation of rehabilitation patients in rehabilitation training, a motion evaluation method capable of processing continuous human rehabilitation training movement data is proposed. First, a motion segmentation method based on the Gaussian mixture model (GMM) is developed to extract single motion repetition from continuous repetitive motion sequences of the same motion. Then, based on relevant a priori knowledge, a multi-feature fusion motion evaluation method combining significant motion feature dynamic time warping (DTW) distance evluation and Gaussian mixture model likelihood evaluation is proposed to perform motion evaluation in both the overall motion feature and local joint information of rehabilitation exercises. The results show that the motion segmentation method can segment the motion data of continuous repetitive motions well, and the correct rate of segmented motions on the dataset reaches more than 95%. The multi-feature fusion motion evaluation method effectively improves the differentiation of motion evaluation between healthy samples and rehabilitation patient samples, so that the motion scores of healthy samples are mainly distributed in the range of 0.93—0.94 on a scale of 0—1, while the motion scores of patient samples are mainly distributed in the range of 0.81—0.89.

Key words: motion evaluation, motion segmentation, Gaussian mixture model (GMM), dynamic time warping (DTW)

CLC Number:

R496

HU Mingxuan, QIAO Jun, ZHANG Zhinan. Segmentation and Evaluation of Continuous Rehabilitation Exercises[J]. Journal of Shanghai Jiao Tong University, 2023, 57(5): 533-544.

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样本类型	样本总数	正确分割数	错误分割数
健康样本	100	97	3
患者样本	100	94	6

比较对象	欧氏距离	马氏距离	DTW 距离	GMM 似然	多特征融合 (本文方法)
不同实验对象	0.335	0.144	0.345	0.357	0.425
同一实验对象	0.523	0.379	0.529	0.584	0.609

样本类型	GMM似然评估		显著特征DTW距离评估		多特征融合评估(本文方法)
样本类型	评分区间	平均值	评分区间	平均值	评分区间	平均值
健康样本	[0.925, 0.941]	0.933	[0.935, 0.949]	0.940	[0.930, 0.944]	0.937
患者样本	[0.854, 0.908]	0.874	[0.774, 0.889]	0.827	[0.817, 0.892]	0.851