Fall Warning Method Based on Multimodal Sensor Fusion and Gait Phase Detection

Falls are a common and serious cause of injury among the elderly and individuals with mobility impairments. In particular, under complex gait conditions, the early detection of imbalance is crucial for fall prevention. To address the limitations of existing methods in fall phase identification and the scarcity of real fall data, this study proposes a fall warning method based on multimodal sensor fusion and gait phase detection. By combining data from plantar pressure sensors and inertial measurement units, a gait phase detection module is introduced to achieve fine division of the gait cycle, enhancing the system's ability to detect early imbalance features. Additionally, a hybrid dataset integrating simulation data with real data is constructed, and multiple linear regression is used to accurately map simulation and real data, mitigating the issue of limited samples. Experimental results demonstrate that the proposed method achieves an accuracy of 94.8%, a recall of 92.8%, and a precision of 94.2%. It further maintains stable performance in cross-subject tests and multi-scenario evaluations, demonstrating strong reliability and generalization capability.