An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

BioMedical Engineering OnLine

Guillermo A Camacho²  Carlos H Llanos¹  Pedro A Berger³  Cristiano Jacques Miosso⁴  Adson F Rocha⁴ 

[1] Mechanical Engineering Department, University of Brasília, Brasília, Brazil

[2] Faculty of Engineering, University of La Salle, Bogotá, Colombia

[3] Computer Science Department, University of Brasília, Brasília, Brazil

[4] Faculty of Gama, University of Brasília, Brasília, Brazil

关键词: Pattern recognition;    Prosthesis;    Particle Swarm optimization (PSO);    Myoelectric control;    Individual principal component analysis (iPCA);    Electromyography;    Artificial bee colony (ABC);   

Others  :  797235
DOI  :  10.1186/1475-925X-12-133

 received in 2013-05-28, accepted in 2013-12-18,  发布年份 2013

【 摘 要 】

Background

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.

Methods

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.

Results

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 (F_0.01,3,72 = 4.0659 > f_AB = 0.09), (2) the levels of factor A have significative effects on the classification error (F_0.02,1,72 = 5.0162 < f_A = 6.56), and (3) the levels of factor B over the classification error are not significative (F_0.01,3,72 = 4.0659 > f_B = 0.08).

Conclusions

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.

【 授权许可】

   
2013 Camacho et al.; licensee BioMed Central Ltd.

【 图 表 】

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