A pattern recognition technique to characterize the differential modulation of co-activating muscles at the performer/environment interface.

The purpose of this research was to develop and test an analytical tool that would recognize and classify the surface electromyographic (EMG) signal of co-activating muscles of the leg into pre-defined patterns of muscle activity: burst, tonic, and tonic-burst. Developed to study the task of landing from a jump in children, the pattern recognition technique (PRT) quantifies the full-wave rectified surface EMG signal over a short-duration sampling window by a single linear regression value. Shifting the sampling window across the data string ultimately defines the signal by a set of regression values that produce the recognizable burst, tonic and tonic-burst patterns on a least-squares surface plot. Statistical comparison of the PRT to the classical combination of threshold detection (+2 S.D. of mean baseline activity) and visual inspection proves the PRT to be more reliable on repeated measures for event detection and classification, with a Kappa statistic of 0.83 compared to 0.54 for threshold detection. Application of the PRT to motor control studies is presented for the regulation of the mechanical response of the leg during impact. Responsiveness of the PRT is tested, issues of accuracy and validity are addressed, and limitations in spatial-temporal resolution are identified.