Bagged regression trees for simultaneous myoelectric force estimation

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 the work of Ameri et al., 2013). 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<;0.05), while for flexion-extension and pronation-supination DOFs, no significant difference was found (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.

[1]  S. Oda,et al.  Motor control for bilateral muscular contractions in humans. , 1997, The Japanese journal of physiology.

[2]  Winnie Jensen,et al.  Estimation of Grasping Force from Features of Intramuscular EMG Signals with Mirrored Bilateral Training , 2011, Annals of Biomedical Engineering.

[3]  Erik Scheme,et al.  Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition–Based Myoelectric Control , 2013, Journal of prosthetics and orthotics : JPO.

[4]  Changmok Choi,et al.  Synergy matrices to estimate fluid wrist movements by surface electromyography. , 2011, Medical engineering & physics.

[5]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[6]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[7]  Winnie Jensen,et al.  Influence of the feature space on the estimation of hand grasping force from intramuscular EMG , 2013, Biomed. Signal Process. Control..

[8]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[9]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[10]  R. N. Scott,et al.  A three-state myo-electric control , 1966, Medical and biological engineering.

[11]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[12]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[13]  T W Williams,et al.  Practical methods for controlling powered upper-extremity prostheses. , 1990, Assistive technology : the official journal of RESNA.

[14]  Ernest Nlandu Kamavuako,et al.  Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms , 2014, IEEE Transactions on Biomedical Engineering.

[15]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[16]  Levi J. Hargrove,et al.  Classification of Simultaneous Movements Using Surface EMG Pattern Recognition , 2013, IEEE Transactions on Biomedical Engineering.

[17]  Dario Farina,et al.  Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training , 2011, IEEE Transactions on Biomedical Engineering.

[18]  T Laurell,et al.  Myoelectric control of a computer animated hand: A new concept based on the combined use of a tree-structured artificial neural network and a data glove , 2006, Journal of medical engineering & technology.

[19]  D. Farina,et al.  Simultaneous and Proportional Estimation of Hand Kinematics From EMG During Mirrored Movements at Multiple Degrees-of-Freedom , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Winnie Jensen,et al.  Surface Versus Untargeted Intramuscular EMG Based Classification of Simultaneous and Dynamically Changing Movements , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Dario Farina,et al.  EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees , 2011, Journal of NeuroEngineering and Rehabilitation.