GoodVibes: Personalized sports injury detection via dual gait vector context-aware learning of human-induced floor vibrations

Sports injuries are common among athletes, often leading to extended recovery and compromised performance. Early and accurate detection of injuries enables timely intervention and improved rehabilitation. However, existing solutions such as wearable devices, computer vision systems, and clinical assessments are limited by complex deployment requirements, user discomfort, or the need for expert supervision. We present GoodVibes , a non-intrusive injury detection system that passively captures gait cycles from footstep-induced floor vibrations on sports facility walkways. Designed for seamless integration into athletic training environments, this system monitors natural walking and captures gait measurements for personalized injury detection. The two key challenges in developing GoodVibes are: (1) overlapping injury signatures caused by musculoskeletal complexity, and (2) inter-person variability reflected in both vibration patterns and personal training or injury history. To address these challenges, GoodVibes introduces three complementary solutions: (1) Variance-Guided Hierarchical Classification, a hierarchical divide-and-conquer approach to reduce uncertainty caused by musculoskeletal interdependence, where adjacent muscles compensate for injured regions, producing overlapping vibration signatures; (2) Dual Gait Vectors, a personalized representation that contrasts reference and observed gait to isolate individual-specific deviations and mitigate uncertainty arising from inter-person variability; and (3) Context-Aware Learning, which integrates personal injury history and specialized sports categories as contextual embeddings to further reduce prediction uncertainty. We evaluated GoodVibes in lab and real-world sports stadium deployments with 21 student-athletes. The system achieved 92.8% accuracy in laboratory settings and 84.2% accuracy in real-world deployments, demonstrating strong performance and potential for long-term passive monitoring in athletic environments.