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.

Dynamics and stability of muscle activations during walking in healthy young and older adults

“…Variability measures do not describe how EMG fluctuations change from one stride to the next, or how multiple muscles coordinate during walking. Clustering or principal component analysis methods quantify multivariate muscle activation patterns (Ivanenko et al, 2004; Jansen et al, 2003), but not across consecutive strides.…”

Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

“…A number of statistical techniques have been proposed to deal with the high dimensionality and uncertainty that is inherent to SEMG data [ 3 , 4 ]. Jansen et al [ 5 ] used a hierarchical clustering procedure to classify different muscle patterns observed in gait, from which they were able to draw inferences about different walking strategies. Intra-class correlation coefficients have been used to identify characteristics of different patient populations [ 6 ].…”

“…Many neurological disorders are associated with increased variability of gait [ 1 , 5 , 9 , 15 ]. This is due to errors in locomotor control caused by dysfunction of specific areas in the CNS.…”

Walking Phase Recognition for People with Lower Limb Disability

“…In [4], twelve features extracted from six parts of the muscle and Discriminant Function Analysis was applied to categorize the walking pattern. To discriminate more complicated patterns, Neuro-Fuzzy Network and Hierarchical Clustering technique were used in [5] and [6]. Even though these studies present various techniques of feature extraction or classification, however, their performance is not guaranteed if the environmental factors change.…”

“…(al)(bl)(cl) x, y, and z-axis mean environmental factor, feature, and separability, respectively. (a2)(b2)(c2) x-axis means feature index(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), and y-axis means separability values for locations, speed, inclination, respectively. In the graph, horizontal marks mean the averages of separabilities, and vertical lines mean the ranges of separability values.…”