Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

Abstract

BackgroundLocomotor control is accomplished by a complex integration of neural mechanisms including a central pattern generator, spinal reflexes and supraspinal control centres. Patterns of muscle activation during walking exhibit an underlying structure in which groups of muscles seem to activate in united bursts. Presented here is a statistical approach for analyzing Surface Electromyography (SEMG) data with the goal of classifying rhythmic "burst" patterns that are consistent with a central pattern generator model of locomotor control.MethodsA fuzzy model of rhythmic locomotor patterns was optimized and evaluated using SEMG data from a convenience sample of four able-bodied individuals. As well, two subjects with pathological gait participated: one with Parkinson's Disease, and one with incomplete spinal cord injury. Subjects walked overground and on a treadmill while SEMG was recorded from major muscles of the lower extremities. The model was fit to half of the recorded data using non-linear optimization and validated against the other half of the data. The coefficient of determination, R2, was used to interpret the model's goodness of fit.ResultsUsing four fuzzy burst patterns, the model was able to explain approximately 70-83% of the variance in muscle activation during treadmill gait and 74% during overground gait. When five burst functions were used, one function was found to be redundant. The model explained 81-83% of the variance in the Parkinsonian gait, and only 46-59% of the variance in spinal cord injured gait.ConclusionsThe analytical approach proposed in this article is a novel way to interpret multichannel SEMG signals by reducing the data into basic rhythmic patterns. This can help us better understand the role of rhythmic patterns in locomotor control.

Rat locomotor spinal circuits in vitro are activated by electrical stimulation with noisy waveforms sampled from human gait

“…For instance, stiffness, viscoelastic properties of muscles, coupling among limb segments and biomechanical constrains, anticipatory adjustments from supraspinal centers, and reflex responses to external perturbations are likely contributors to the noise within in vivo EMGs (Thrasher et al. ). Other forms of variability in human EMG signals are attributable to specific muscle pennation and fiber composition (Johnson et al.…”

“…However, intrinsic variability of FListim mainly corresponds to the firing profile of motoneurons within the same pool during FL (Berg et al 2007), while for the EMG of real locomotion in a volunteer, additional nonlinear sources of variability need to be considered. For instance, stiffness, viscoelastic properties of muscles, coupling among limb segments and biomechanical constrains, anticipatory adjustments from supraspinal centers, and reflex responses to external perturbations are likely contributors to the noise within in vivo EMGs (Thrasher et al 2011). Other forms of variability in human EMG signals are attributable to specific muscle pennation and fiber composition (Johnson et al 1973) or to the proximity of EMG electrodes to the muscle innervation (De Luca 1997).…”

Changes in Muscle Coordination Following Robot-assisted Gait Training in Hemiparetic Stroke

“…In our previous work, we measured values of IR in normal, healthy adults between 0.70 and 0.80 [8]. Perfect rhythmicity (IR=1.00) represents a set of perfectly periodic SEMG signals with no deviations.…”

“…Rhythmicity was assessed by processing and coding SEMG signals using fuzzy sets according to a classification procedure previously described [8]. This method was designed to represent multiple muscle activation signals as a recurrent sequence of four basic burst patterns in the manner of the CPG.…”

“…Locomotor training focuses on adaptation of the Central Pattern Generator (CPG), which is a largely autonomous neural circuit that produces cyclical bursts of pre-determined muscle activation signals [6,7]. The output of the CPG can be measured in the extremities using surface electromyography (SEMG), from which rhythmic patterns can be identified using a statistical classification technique [8]. This way, we can evaluate the degree to which multiple muscles act in a stable, rhythmic, coordinated manner.…”