  This work has been conducted in the context of pattern-recognition-based
control for electromyographic prostheses. It presents a k-nearest neighbour
(kNN) classification technique for gesture recognition, extended by a
proportionality scheme. The methods proposed are practically implemented and
validated. Datasets are captured by means of a state-of-the-art 8-channel
electromyography (EMG) armband positioned on the forearm. Based on this data,
the influence of kNN's parameters is analyzed in pilot experiments. Moreover,
the effect of proportionality scaling and rest thresholding schemes is
investigated. A randomized, double-blind user study is conducted to compare the
implemented method with the state-of-research algorithm Ridge Regression with
Random Fourier Features (RR-RFF) for different levels of gesture exertion. The
results from these experiments show a statistically significant improvement in
favour of the kNN-based algorithm.
