Prediction of movement onset and direction based on muscle activity during reaching movements

Electromyography as a technology allows one to be able to measure muscle activity, which in turn can be used to detect movement direction and onset. The process for this classification problem often involves a multitude of various extracted features and classification techniques, that differ a lot across different scientific papers. This thesis analyzed different features and classifiers and tackled a center out reaching task to determine a good workflow for classifying arm movement direction. The data was recorded with 6 sEMG electrodes placed on the upper arm of 5 healthy participants. The different experiments show good classification accuracy of over 96 % in a reaching task with 16 targets placed in a circle within reaching distance. The results also show that the classification accuracy did not differ a lot between features. The individual EMG channels also display high correlation, which suggests a possible reduction in necessary electrodes. Classification accuracy before movement onset also only dropped by 2 % compared to the accuracy of the whole time window of a reaching motion. This seems especially vital to ensure proper support via prosthesis or orthesis for people with heavily impaired movement