{'fraction_non_alphanumeric': 0.02099737532808399, 'fraction_numerical': 0.0, 'mean_word_length': 5.577586206896552, '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}}
Time-Varying Multicomponent Signal Modeling for Analysis of Surface EMG Data

We present a novel approach to surface EMG data characterization by using time-varying multicomponent signal modeling. An EMG signal is described as a set of stationary non-harmonically related sinusoids (signal components) whose time-varying bandwidth is modeled by polynomials. The polynomial coefficients, estimated from a set of linear equations, capture the relationship between the instantaneous frequency and amplitude for individual signal components. It is proposed that such a compact EMG signal modeling may be a good candidate for a number of applications in surface electromyography: compression, muscle activity detection and low-bias conduction velocity, to name a few.