A case study on tuning artificial neural networks to recognize signal patterns of hand motions

This paper presents the development of artificial neural networks (ANN) as pattern recognition systems to classify surface electromyography signals (sEMG) into nine select hand motions from seven subjects. Multiple networks were designed to determine how well a network could adapt to signals from different subjects. This was achieved by developing multiple networks with different combinations of the volunteers for training. Each network was tested with signals from all volunteers to determine how well they could adapt to new subjects. It was found that ANNs trained using only one or two subjects would perform exceptionally well when tested with signals from the same subjects but relatively poorly when tested with signals from new subjects. As the number of subjects used for training increased, the ability of the network to accurately classify the signals from the trainees decreased but their ability to adapt to signals from new subjects increased. Solely based on these results, it can be inferred that ANNs developed using signals from a large amount of subjects could be used to accurately classify signals from completely new subjects. Research presented in this paper has potential to be further developed as a basis for utilizing sEMG as control signals in electric devices such as myoelectric prosthesis or humanoid control.