The Rhythm of Identity: Natural Biological Gait Recognition

This 18-lecture course examines natural biological gait recognition, the process of identifying individuals through the biomechanics, rhythm, posture, and movement signatures of human walking. The series begins with the inverted pendulum model of locomotion, the stance and swing phases of the gait cycle, and the extraction of spatiotemporal features such as step length, cadence, and double support time. Subsequent lectures cover upper-body posture, arm swing, and spinal curvature as additional identifiers, then move to camera-based silhouette methods like Gait Energy Images and model-based joint tracking. Wearable inertial sensors are presented as a means for continuous authentication, while clinical gait analysis demonstrates how the same metrics detect neurological conditions and track recovery. Machine learning sections detail the shift from handcrafted features to convolutional and recurrent networks, including the use of large datasets such as CASIA and OU-ISIR. Real-world challenges are addressed through discussions of view invariance, surface variation, clothing covariates, and load carrying. The course also examines affective gait changes linked to emotional state and explores multi-modal fusion with face or height data. Final lectures focus on privacy-preserving techniques, consent requirements, and ethical limits, emphasizing that recognition systems must be designed to respect individual movement and prevent unauthorized surveillance.