{'fraction_non_alphanumeric': 0.01267605633802817, 'fraction_numerical': 0.0028169014084507044, 'mean_word_length': 5.463636363636364, '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': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
EMG Spectral Analysis for Prosthetic Finger Control

Electromyography monitoring is an effective method for prosthetic limb movement control. However, existing algorithms do not offer satisfactory accuracies. In this paper, we propose an accurate method to extract features from EMG that can be used in prosthetic finger movement control. The proposed algorithm estimates power spectral density of EMG signal using an autoregressive model to extract features. The key advantage of our method is in reducing the dimensionality of EMG signal and capturing its frequency content simultaneously. The proposed method has achieved above 89% accuracy in classifying ten finger movements archived in a public database.