Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

This study introduces a new statistical method to analyze muscle activity during walking, using Surface Electromyography (SEMG). The goal is to identify and classify rhythmic patterns in muscle activation that support the idea of a central pattern generator (CPG) controlling movement. The method uses a 'fuzzy model' to represent rhythmic patterns, which is tested using SEMG data from healthy individuals and those with gait abnormalities like Parkinson's Disease and spinal cord injury. By understanding these basic rhythmic patterns, researchers hope to gain insights into how the central nervous system controls walking and how this control is affected by neurological conditions.

• 1
  The fuzzy model, using four rhythmic burst patterns, effectively accounted for approximately 70-83% of the variability in muscle activation during treadmill walking and 74% during overground walking in healthy individuals.
• 2
  The model explained 81-83% of the variance in the Parkinsonian gait, suggesting a strong rhythmic component, but only 46-59% of the variance in spinal cord injured gait, indicating more irregular muscle activation patterns.
• 3
  When the model was expanded to include five burst functions, one of the functions was found to be redundant, suggesting that four basic patterns are sufficient to capture the primary rhythmic components of muscle activation during gait.