Tripping-induced Fall Detection Using Dual-torso Inertial Measurement Units: An Experimental Evaluation with Long Short-term Memory

With the growing aging population, the demand for reliable fall detection systems is increasing.Owing to safety concerns, real-world fall data-especially for tripping-induced falls-are often limited in existing studies.To address this limitation, we developed a largescale fall-protection system and conducted controlled experiments specifically targeting tripping falls, ensuring subject safety throughout.Subjects wore two inertial measurement units mounted on the torso-one on the chest and the other on the abdomen-and performed both tripping falls and fall-like behaviors.These wearable inertial sensors continuously captured torso motion signals, providing time-series acceleration and angular velocity data for subsequent analysis.On the basis of the collected time-series data, three long short-term memory-based models were developed: two single-sensor models and one dual-sensor model.A leave-one-subject-out crossvalidation approach was applied, and a low sigmoid decision threshold together with a consecutive-window decision rule was adopted to reduce missed detections.Experimental results demonstrated high classification performance across all models, with the dual-sensor model achieving the best accuracy, precision, recall, and F1-score.These findings confirm the effectiveness of the proposed method in distinguishing tripping falls from similar daily activities.