title: Multidimensional EMG-based assessment of walking dynamics.
abstract: The electromyogram (EMG) provides a measure of a muscle's involvement in the execution of a motor task. Successful completion of an activity, such as walking, depends on the efficient motor control of a group of muscles. In this paper, we present a method to quantify the intricate phasing and activation levels of a group of muscles during gait. At the core of our method is a multidimensional representation of the EMG activity observed during a single stride. This representation is referred to as a "trajectory." A hierarchical clustering procedure is used to identify representative classes of muscle activity patterns. The relative frequencies with which these motor patterns occur during a session (i.e., a series of consecutive strides) are expressed as histograms. Changes in walking strategy will be reflected as changes in the relative frequency with which specific gait patterns occur. This method was evaluated using EMG data obtained during walking on a level and a moderately-inclined treadmill. It was found that the histogram changes due to artificially altered gait are significantly larger than the changes due to normal day-to-day variability.
mesh_terms: D000465:Algorithms; D000843:Ankle Joint / Q000502:physiology*; D016000:Cluster Analysis; D004576:Electromyography / Q000379:methods*; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D007719:Knee Joint / Q000502:physiology*; D007866:Leg / Q000502:physiology; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D010507:Periodicity; D016138:Walking / Q000502:physiology*