基于几何与物理特征融合的智能下肢假肢运动意图识别

传统的意图识别方法所用传感器数量及种类较多,特征向量维数偏高,统计特征对短时样本具有不稳定性.将关节角表示的几何特征与加速度、角速度表示的物理特征有机融合并应用于智能下肢假肢的运动意图识别.首先,利用惯性测量单元于健侧大腿、小腿处采集的摆动相前期短时时序数据解算膝关节角,以获取大腿、小腿绕关节轴的转动特性;其次,对物理...

Movement intention recognition of intelligent lower limb prosthesis based on the fusion of geometric and physical features

A large number of sensors are used in the method of traditional intention recognition. The feature space composed of data has high dimension, and the statistical features are unstable for short-term samples. In this paper, the geometric features represented by the joint angles and the physical features represented by the acceleration and angular velocity are organically integrated, and they are used in the motion intention recognition of the intelligent lower limb prosthesis. Firstly, the knee joint angle is calculated by using the short time series data collected from the healthy thigh and calf in the early swing phase by the inertial measurement unit, so as to obtain the rotation characteristics of the thigh and calf around the joint axis. Then, the mean value and variance of physical features are extracted to reflect the mean level and dispersion degree of short-term data, and the slopes of the maximum and minimum of geometric features are extracted to reflect the local change rate of short-term data and make up for the instability of statistical features. Finally, the geometric features and physical features are fused, and the support vector machine is used to classify 13 daily behaviors. The experimental results show that the recognition rate of 5 kinds of steady states including walking, stair ascent, stair descent, ramp ascent and ramp descent reaches 96.9%. The recognition rate of 8 kinds of transitional states reaches 97.1%. The recognition rate of 13 kinds of states reaches 94.3%. Only the data of two sensors on the healthy side are used to form a 25-dimensional hybrid feature by feature fusion, which achieves rapid dimensionality reduction and reduces the algorithm complexity.