Adaptive Surface Electromyography Normalization for Long-Duration Recordings

摘要

Long-duration surface electromyography (sEMG) recordings are not considered in recent consensus on the appropriate methods for sEMG normalization. Here we find that sEMG data normalized by the gold standard, maximum voluntary contraction, fails to appropriately represent the amplitude recorded from walking bouts over an 18-hour period, suggesting that normalization reference values may not remain valid over long periods. To address this limitation, we present a new adaptive method for sEMG normalization that leverages data collected during typical daily activities. We explore several candidate daily activities for performing this normalization, and assess their ability to resolve expected sEMG amplitude changes with stride time and activity intensity. Normalization to walking, and particularly to a self-selected comfortable speed, yields the best results.