Optimal automatic detection of muscle activation intervals.

A significant challenge in surface electromyography (sEMG) is the accurate identification of onsets and offsets of muscle activations. Manual labelling and automatic detection are currently used with varying degrees of reliability, accuracy and time efficiency. Automatic methods still require significant manual input to set the optimal parameters for the detection algorithm. These parameters usually need to be adjusted for each individual, muscle and movement task. We propose a method to automatically identify optimal detection parameters in a minimally supervised way. The proposed method solves an optimisation problem that only requires as input the number of activation bursts in the sEMG in a given time interval. This approach was tested on an extended version of the widely adopted double thresholding algorithm, although the optimisation could be applied to any detection algorithm. sEMG data from 22 healthy participants performing a single (ankle dorsiflexion) and a multi-joint (step on/off) task were used for evaluation. Detection rate, concordance, F

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Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.