Fall Monitoring With Single IMU: A Large-Scale Dataset and a Novel Dual-Branch Network

With the rapid growth of the elderly population, fall accidents have received increasing attention due to their serious health hazards. Pre-impact fall detection (PIFD) based on wearable sensors emerges as a promising approach for proactive fall prevention in healthcare monitoring. In this research, based on Inertial Measurement Units (IMUs), we construct and publicly provide a large-scale motion dataset named FallTL, which includes falls and activities of daily living (ADLs) collected from multiple body segments. Furthermore, we develop STA-Net, a novel Spatial-Temporal Attention Network to perform PIFD based on IMU data from a single body segment. STA-Net incorporates a dual-branch architecture: a temporal attention branch that models temporal signal dependencies and a spatial attention branch that captures cross-modality feature interactions, enabling robust representation learning from sensor data. We evaluate STA-Net across three datasets and it achieves advantageous performance and comparable lead time under cross-subject validation, outperforming state-of-the-art baselines. In addition, our analysis further investigates the influence of sensor placement and data modality on detection performance. These results indicate that accurate and robust PIFD is feasible with minimally obtrusive, single-location sensor setups, offering practical implications for wearable fall monitoring systems.