Faculty of Engineering, University of La Salle, Bogotá, Colombia.
Biomed Eng Online. 2013 Dec 27;12:133. doi: 10.1186/1475-925X-12-133.
The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored.
The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A1 (the classification error) and A2 (the correlation factor). Otherwise, the B factor has four levels, specifically B1 (the Sequential Forward Selection, SFS), B2 (the Sequential Floating Forward Selection, SFFS), B3 (Artificial Bee Colony, ABC), and B4 (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS.
A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F0.01,3,72 = 4.0659 > fAB = 0.09), (2) the levels of factor A have significative effects on the classification error (F0.02,1,72 = 5.0162 < fA = 6.56), and (3) the levels of factor B over the classification error are not significative (F0.01,3,72 = 4.0659 > fB = 0.08).
Considering the classification performance we found a superiority of using the factor A2 in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm.
肌电信号的信息可被肌电控制系统（MCS）用于驱动假肢。这些设备允许执行那些被截肢者无法完成的动作。MCS 开发的最新技术基于使用个体主成分分析（iPCA）作为分类器预处理的一个阶段。iPCA 预处理意味着尚未深入探索的优化阶段。
本研究在 iPCA 阶段考虑了两个因素：A（适应度函数）和 B（搜索算法）。A 因素包括两个水平，即 A1（分类误差）和 A2（相关因子）。此外，B 因素有四个水平，分别为 B1（顺序前向选择，SFS）、B2（顺序浮动前向选择，SFFS）、B3（人工蜂群，ABC）和 B4（粒子群优化，PSO）。本工作评估了 A 和 B 因素之间的每一种八种可能组合对 MCS 分类误差的影响。
对计算出的分类误差进行了两因素方差分析，结果表明：（1）交互作用对分类误差没有显著影响（F0.01，3，72=4.0659>fAB=0.09）；（2）因素 A 的水平对分类误差有显著影响（F0.02，1，72=5.0162<fA=6.56）；（3）因素 B 对分类误差的水平没有显著影响（F0.01，3，72=4.0659>fB=0.08）。
考虑到分类性能，我们发现使用因素 A2 与任何因素 B 水平相结合具有优越性。关于时间性能，分析表明，PSO 算法的性能至少比其最佳竞争对手好 14%。这种行为是在搜索算法的特定参数配置集下观察到的。未来的工作将研究这些参数对分类性能的影响，例如降维向量的长度、粒子群优化算法中的粒子和蜜蜂的数量以及 ABC 算法中的优化搜索循环的限制。
