Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors
Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors
复制标题
DOI：
10.1109/jas.2017.7510619
复制
发表时间：
2017-10-01
影响因子：
11.8
通讯作者：
Yang, Peng
中科院分区：
文献类型：
--
作者：
Liu, Zuojun;Lin, Wei;Yang, Peng
基于下肢运动的规律性，提出了一种膝上假肢的意图模式识别方法。针对基于肌电信号的识别器的缺陷，提出了一种纯机械传感器的下肢运动意图模式识别方法。传感器系统由安装在假肢接受腔上的加速度计、陀螺仪和安装在鞋底下的两个压力传感器组成。为了补偿假肢控制中的延迟，在站立阶段的信号被用来预测在摆动阶段的地形和速度。具体地，意图模式识别器根据行走速度和地形的笛卡尔乘积利用类内相关系数（ICC）。此外，传感器数据融合的Dempster-Shafer的理论。在前一步的基础上，利用隐马尔可夫模型（HMM）对运动状态进行实时识别。所提出的方法可以推断假肢使用者在不同地形上行走的意图，包括平地、上楼梯、下楼梯、上下坡道。实验结果表明，该意图模式识别器对五种典型地形模式的识别率为5.8%。这项研究的结果预计将大大提高动力膝上假肢的控制性能。
Based on the regularity nature of lower-limb motion, an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram (EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient (ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model (HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground, stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of (5.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.