Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods

Abstract Objective . Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method considered the ‘gold standard’ in detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it unclear if they sensitive enough accurately detect EMG relating them short-duration motor events. Approach This study aimed determine: (1) automatic could be used interchangeably with detecting onsets (2) Teager–Kaiser energy operator (TKEO) a conditioning step would improve accuracy popular methods. three methods: approximated generalized likelihood ratio (AGLR), TKEO, threshold-based were examined against method. signals from fast, explosive, slow, ramped isometric plantarflexor contractions evaluated using each technique. Main results For explosive contractions, TKEO was best-performing method, low bias level (4.7 ± 5.6 ms) excellent intraclass correlation coefficient (ICC) 0.993, however wide limits agreement (LoA) (?6.2 +15.7 ms). AGLR smallest (11.3 32.9 ICC (0.983) produced LoA (?53.2 +75.8 detection, inclusion improved inter-rater intra-rater reliability across contraction types compared without conditioning. Significance In conclusion, not applied event duration. To attain needed, recommended. before recommended reliability.