Pattern recognition master thesis

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Pattern recognition master thesis

For myoelectric control, there is a need to interpret the muscle signals in order to decode the underlying movement.

Master of Science in Computer Science, MS | Davenport University

Recently, myoelectric control strategies have employed pattern recognition algorithms to decode complex movement patterns. The goal is to create intuitive control systems where the artificial movement is controlled as with the natural limb.

A significant amount of work done in myoelectric pattern recognition research focuses on the pre-processing stage of feature extraction. This aims to reduce the electromyography EMG signal to a set of descriptive values.

However, these engineered features may not be ideal for pattern recognition applications. The aim of this thesis was to evaluate the performance of deep learning algorithms for myoelectric control, without prior feature extraction.

Three different network architectures were tested: To evaluate the performances, the necessary data was obtained from the open source Ninapro dataset 7.

The experiments showed that the RNN provided the best result, with a median accuracy of These accuracies were averaged across ten different subjects and two groups of movements. With the system used for testing, the time an input window took to be classified was approximately 20 ms.

Even though these tests were offline, the computation should be fast enough to implement in online control systems as well.III.

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Pattern recognition master thesis

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Department of Electrical Engineering and Computer Science < Case Western Reserve University