Abstract
A novel application of bootstrap aggregating (bagged) regression trees is proposed for simultaneous force estimation of multiple degrees of freedom (DOFs). Ten able-bodied subjects participated and wrist flexion-extension, abduction-adduction, and pronation-supination were investigated (data from [17]). The estimation accuracies were compared to those of the widely used multilayer perceptron artificial neural networks (ANNs). The bagged trees outperformed the baseline ANNs, slightly but significantly, in abduction-adduction (p 0.1) between the bagged tress and ANNs. The results suggest that bagged regression trees can be an alternative approach for potential use in simultaneous myoelectric control.
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