SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis.

Area of Science:

• Biomechanics
• Artificial Intelligence
• Medical Technology

Background:

• Human gait is influenced by neurological, orthopedic, and pathological factors, making gait analysis crucial for diagnostics and performance evaluation.
• Current gait analysis technologies face challenges in balancing cost, accuracy, speed, and convenience.
• There is a need for accessible, low-cost solutions to support individuals with motor impairments.

Purpose of the Study:

• To develop a novel, automated approach for motion characterization using artificial intelligence.
• To enable real-time, non-invasive, and markerless gait analysis.
• To provide a cost-effective solution for improving the quality of life for individuals with motor impairments.

Main Methods:

• An artificial intelligence-based system was developed for automated motion characterization.
• The system performs real-time, markerless analysis of the gait cycle.
• Gait metrics were compared between the proposed system and traditional motion tracking methods.

Main Results:

• The novel automated procedure enables rapid diagnosis and reduces human error in gait analysis.
• The AI-driven system demonstrated effectiveness in real-time gait analysis.
• Comparison with existing systems validated the proposed methodology's accuracy.

Conclusions:

• The proposed AI-powered, markerless gait analysis system offers a promising advancement in the field.
• This technology has the potential to improve diagnosis, treatment assessment, and performance enhancement.
• The system provides a low-cost, convenient, and accurate solution for diverse gait analysis applications.