A Real-Time Classification System for Upper Limb Prosthesis Control in MATLAB
In this paper we present a MATLAB tool for processing both EMG and NIR sensor signals in real-time in order to provide a fully operational tool for clinical testing. After a short training phase, the decision tree classifier produces output for actuating a Michelangelo hand by Ottobock Healthcare. To validate the system design, it was tested with four probands performing wrist flexion, wrist extension and fist hand movement patterns. After a training phase, features were extracted in real-time from either the EMG or NIR sensor data for classification with the model created during the training phase. In this setup, NIR sensor data alone proved to be sufficient for distinguishing three hand movement patterns with two sensors. The classification accuracy is equal or better to standard EMG data recorded from the same sensor pick-up area on the forearm.
KeywordsProsthesis control Machine learning EMG signal NIR signal
The authors are grateful to Prof. Klaus Buchenrieder of the Universität der Bundeswehr for his support of their research as well as Otto Bock Healthcare for supplying the Michelangelo hand employed for testing the control scheme.
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