{'fraction_non_alphanumeric': 0.017735334242837655, 'fraction_numerical': 0.0, 'mean_word_length': 5.57847533632287, '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}}
A muscular fatigue index based on the relationships between superimposed M wave and preceding background activity

A practical muscle fatigue index is studied in this paper using the correlation between the instantaneous frequencies (IF's) of the superimposed M wave and the mean power frequency (MPF) of the preceding background activity. A superimposed M wave is an M wave elicited during a sustained contraction and was recently introduced for studying muscle fatigue. The authors investigated the details of the distribution of a feature vector (mpf, if) in two-dimensional space. Their experimental results showed that MPF and IF's were closely correlated during the first phase of a short-term high-level sustained voluntary contraction and then became uncorrelated or sometimes showed negative correlation as muscular fatigue progressed. Combining the correlation coefficients and conventional myoelectric (ME) parameters, such as the MPF and the average rectified value of ME signals, the authors propose a fuzzy rule based muscular fatigue index that can be used for managing the inevitable variability among individual subjects collected as a group. Introducing fuzzy inference seemed effective, but further studies including detailed investigation of the level of voluntary effort, the muscle fiber type composition, and metabolic by-products will be needed to customize the membership functions and fuzzy rules more appropriately in each practical field.