Quantification of the dynamic properties of EMG patterns during gait.

A technique for analyzing and comparing the dynamic properties of electromyographic (EMG) patterns collected during gait is presented. A gait metric is computed, consisting of both magnitude (amplitude) and phase (timing) components. For the magnitude component, the processed EMG pattern is compared to a normative EMG pattern obtained under similar walking conditions, where the metric is incremented if the muscle is firing during expected active regions or is silent during expected inactive regions. The magnitude metric is penalized when the EMG is silent during phases of expected activity or when the EMG is active in regions of expected inactivity. The phase component of the metric computes the percentage of the gait cycle when the muscle is firing appropriately, that is, active in expected active regions and silent in expected inactive regions. The magnitude and phase components of the metric are normalized and combined to yield the EMG pattern that demonstrates the closest characteristics compared to normative gait data collected under similar walking conditions. Using experimental data, the proposed gait metric was tested and accurately reflects the observed changes in the EMG patterns. Clinical uses for the gait metric are discussed in relation to gait therapies, such as determining optimal gait training conditions in individuals following stroke and spinal cord injury.