Human Gait Modeling and Analysis Using a Semi-Markov Process With Ground Reaction Forces.

Area of Science:

• Biomechanics
• Rehabilitation Engineering
• Medical Informatics

Background:

• Gait pattern modeling and evaluation are crucial for gait assessment and rehabilitation.
• Existing methods may lack convenience or real-time capabilities.

Purpose of the Study:

• To present a convenient, real-time method for gait modeling, analysis, and evaluation.
• To characterize individual gait patterns and assess abnormalities.

Main Methods:

• Utilized smart insoles to measure ground reaction forces (GRFs).
• Modeled human gait as a semi-Markov process (SMP) based on foot-ground contact states.
• Derived gait features from the SMP model and proposed Abnormal Evaluation Indices (AEI).

Main Results:

• Successfully obtained and evaluated gait patterns for normal, age-related, and pathological gaits.
• Demonstrated the effectiveness of the proposed AEI for gait abnormality assessment.
• Validated results against a video-based gait abnormality rating scale.

Conclusions:

• The proposed smart insole-based method provides a convenient and effective approach to real-time gait analysis.
• The semi-Markov process (SMP) model and AEI are valuable tools for characterizing and assessing gait abnormalities.