Analysis of muscle activity during gait cycle using fuzzy rule-based reasoning

The purpose of this study was to determine the patterns of muscle activation as outcome measures of the ground reaction forces during normal walking tasks using optical motion analysis capture system, instrumented treadmill and electromyography (EMG), respectively. The recognition of the muscle patterns during gait dynamics offers insight into the control of skeletal position, joint stiffness, vibrations of the soft tissue packages, stability during ground contact, and propulsion for the movement task. Sixteen able-bodied participants were recruited to walk on a dual-belt instrumented treadmill with embedded force plates. A fuzzy rule-based reasoning algorithm for recognizing the activation patterns of the lower extremity muscles during normal walking maneuvers within the seven gait phases was developed. The resulting recognition will enable the determination of alterations in the locomotor control system, contribute to suggest symptoms of a neurological disease, disease severity, and also indications of improvements in response to therapeutic interventions.