基于PSoC和DP-PSO-SVM的便携式假肢控制系统

针对三自由度假肢阈值控制方式存在不直观、灵活性差的缺陷,控制效果较好的模式识别控制器存在便携性差、实用性差等问题,提出一种基于PSoC在线模式识别的肌电假肢控制系统设计方案.采用低功耗芯片PSoC作为主控制器,设计了一套便携式四通道sEMG (表面肌电信号)采集系统,采用双群粒子群优化算法改进的支持向量机(DP-PSO-SVM)构建分类识别器,并通过假肢驱动器实现假肢在线模式控制.实验结果表明:采用DP-PSO-SVM算法比采用标准粒子群SVM (PSO-SVM)算法构建的分类器识别精度提高4%,达到96.7%;该控制器对6种动作的在线识别率达到96.3%,且符合实时性要求.

Portable Prosthesis Control System Based on PSoC and DP-PSO-SVM

Considering that three-degree-of-freedom artificial limbs based on the threshold control method are not intuitive and flexible and that pattern-recognized controllers with higher identification precision have a poor portability and usability, an online pattern-recognized control system based on programmable system-on-chip (PSoC) is proposed. The PSoC, a kind of low-power processor, is used as the main controller, and it is also used to design a portable four-channel surface electromyography (sEMG) signal acquisition system. A DP-PSO-SVM (dynamic programming-particle swarm optimization-support vector machine) algorithm is proposed for movement recognition, and then the prosthesis can be controlled online through a prosthetic actuator. Experimental results show that the classifier using the DP-PSO-SVM algorithm has a recognition rate of 96.7%, which is 4% higher than that of the classifier based on PSO-SVM algorithm; the controller exhibited an online recognition rate of 96.3% for six actions, and it meets real-time requirements.