ROBUST TRAINING AND CONTROL STRATEGIES FOR THE GRASP TYPE SELECTION OF HAND PROSTHESES

Man/machine interfaces are often designed on patient training data (e.g. electromyographic or electroencephalographic time series) from one session. The design process adapts parameters for signal processing and classification. The automatically adapted classification routine delivers good results on this data set but the man/machine interface might show a lack of classification accuracy (robustness) at following sessions and at activities of daily living. This article discusses the underlying effects and presents a method for robust design. A comparison with a common design delivers conclusions about the accuracy of validation techniques. The new methods are applied to electromyographic patient data for the control of a hand prosthesis.