A new hierarchical approach for simultaneous control of multi-joint powered prostheses

Advanced upper-limb prostheses capable of actuating multiple degrees of freedom (DOF) are now commercially available. Pattern recognition based algorithms that use surface electromyography (EMG) signals measured from residual muscles show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to sequential control of each DOF. This study introduces a hierarchy of linear discriminant analysis (LDA) classifiers arranged to provide simultaneous DOF control. This approach and two other simultaneous control strategies were evaluated using healthy subjects controlling up to four DOFs, where any two DOFs could be controlled simultaneously. The new hierarchical approach was the most promising with classification errors at or below 15% on average for discrete and combined motions. The classification performance was significantly better (p <; 0.05) than using a single LDA classifier trained to recognize both discrete and combined motions or classifying each DOF using a set of parallel classifiers. The high accuracy of the hierarchical approach suggests that pattern recognition techniques can be extended to permit simultaneous control, potentially allowing amputees to produce more fluid, life-like movements, ultimately increasing their quality of life.