Abstract
Patient satisfaction with body-powered myoelectric upper limb prostheses is limited, often resulting in device abandonment. Multifunctional hand prostheses are one potential solution to increase patient acceptance. These require sophisticated control schemes like pattern-recognition-based approaches involving classification of myoelectric signals (MES). To allow fast and flexible evaluation of prosthesis control approaches, a prototyping environment based on the Raspberry Pi and MATLAB/Simulink was created. It supports commonly applied features like RMS and zero crossings as well as classification methods like Naive Bayesian and Support Vector Machine classifiers. After classifier training with a custom MATLAB application, MES can be classified in real-time and the results employed for prosthesis actuation. The setup was tested with five participants for controlling a Michelangelo Hand. Over 90 % of movements were correctly identified for three classes from two channel EMG data.
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Acknowledgement
We are grateful to M.Sc. Lars Achterberg for his work in implementing the Raspberry Pi ADC software and MATLAB/Simulink model [1]. Furthermore, we like to thank M.Sc. Sebastian Preibisch, M.Sc. Steve Wilhelm and mgr inż Sławomir Wojciechowski for taking part in the study.
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Attenberger, A., Buchenrieder, K. (2015). MATLAB/Simulink-Supported EMG Classification on the Raspberry Pi. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_56
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