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Abstract Real-world gait recognition systems must continuously enroll new users while coping with evolving gait patterns over time. Conventional model training methods cannot adapt to such ongoing changes, leading to catastrophic forgetting and performance degradation. Moreover, in practical scenarios, sensor wearing positions vary across users and usage habits (e.g., thigh or handheld).
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