为提高对康复训练动作反馈的准确性和客观性以及康复患者在康复训练中的积极性和自律性,建立一种能够处理人体连续康复训练动作数据的动作评估方法.首先,提出一种基于高斯混合模型的动作分割方法,从同一动作的连续重复运动序列中提取单次动作数据.其次,根据相关先验知识,提出结合显著运动特征动态时间规整距离评估与高斯混合模型似然评估的多特征融合动作评估方法,从康复训练动作的整体动作与局部关节信息两方面进行动作评估.结果表明:动作分割方法能够很好地分割连续重复动作的运动数据,在数据集上分割动作的正确率达到95%以上;多特征融合动作评估方法有效地提高健康样本与康复患者样本之间动作评估的区分度,使得健康样本动作分数在0~1的评分范围内主要分布在0.93~0.94的得分区间,患者样本则主要分布在0.81~0.89的得分区间.

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.