{'fraction_non_alphanumeric': 0.013761467889908258, 'fraction_numerical': 0.013761467889908258, 'mean_word_length': 4.705882352941177, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Development of a myoelectric control scheme based on a time delayed neural network

Presented in this work is a possible myoelectric control scheme for a rehabilitation robotic application. The control input is from a time delayed neural network (TDNN). The input to the TDNN is four electromyographic (EMG) signals associated with the movement of the elbow and shoulder joints. The output of the TDNN is the joint position of the elbow and the joint position of the shoulder in the sagittal plane. The results presented here show the possibility of controlling multiple degrees of freedom at once. Prior work has shown that the optimal delay for accurate position prediction from a TDNN was 875ms with a 125ms interval, but this work shows that a delay of 300ms and a 100ms interval achieves similar results. This points to the feasibility of a TDNN based control scheme.