Research on motion intent recognition method for human joint angle prediction
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摘要:
为了弥补离散状态识别方法在运动过程预测上的不足,提出了一种面向人体关节角度预测的运动意图识别方法。 围绕 A 型超声探头设计了驱动电路和回波采集程序用于测量肌肉厚度,收集了 6 名志愿者的运动数据,经过对同一运动过程中肌肉 厚度和关节角度的数据拟合,定制化地生成了映射关系模型,得到决定系数 R 2 的平均值为 0. 916 9,显示出较好的匹配度,将模 型固化到程序中,系统的预测值输出响应频率可以达到 30 Hz,表明该方法可以跟踪过程中的连续状态变化识别人的运动意图, 相比于离散状态识别方法,可以有效提升识别精度和实时性能。
Abstract:
In order to make up the deficiency of discrete state recognition in motion process prediction, a motion intent recognition method for human joint angle prediction is proposed. The driver circuit and echo acquisition program were designed for the A-mode ultrasound probe to measure the muscle thickness. The motion data of 6 volunteers were collected. A customized mapping relation model was generated after fitting the data of muscle thickness and joint angle during the same movement. The average value of the coefficient of determination R 2 is 0. 916 9, which shows a good matching degree. The system’s output response frequency of predictive value can reach 30 Hz when the model was solidified into the program, which indicates that the method can track the continuous state changes in the process and recognize the human motion intent. Compared with the discrete state recognition method, it can effectively improve the recognition precision and real-time performance.
