Identification of Human Gait using Fuzzy Inferential Reasoning

The restoration of healthy locomotion (gait) after stroke, traumatic brain injury, and spinal cord injury, is a major task in neurological rehabilitation. The rehabilitation process is labor intensive. Patient evaluation is often subjective, foiling determination of precise rehabilitation goals and assessment of treatment effects. To date it is the experienced clinician who continues to perform functional gait assessment and training in the absence of virtually any technological assistance. This paper introduces an algorithm capable of identifying human gait patterns. The fuzzy inferential reasoning uses typical joint angle trajectories to identify varying gait patterns. The algorithm will, thus, offer doctors, therapists, and patients a significant tool to assess the efficacy and outcomes of medical rehabilitation therapies and practices.