Email updates

Keep up to date with the latest news and content from JNER and BioMed Central.

Open Access Methodology

Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

Timothy A Thrasher1*, John S Ward1 and Stanley Fisher2

Author Affiliations

1 Dept of Health and Human Performance, (Center) for Neuromotor and Biomechanics Research, University of Houston, Houston, TX, USA

2 The Methodist Neurological Institute, Houston, TX, USA

For all author emails, please log on.

Journal of NeuroEngineering and Rehabilitation 2011, 8:65  doi:10.1186/1743-0003-8-65

Published: 8 December 2011



Locomotor 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.


A 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.


Using 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.


The 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.

Surface electromyography; gait; central pattern generator; fuzzy analysis