MSTG-Transformer: Multivariate Spatial-Temporal Gated Transformer Model for 3D Skeleton Data-based Fall Risk Prediction

As the aging population continues to grow, falls among older adults have become a significant public health concern worldwide. Data-driven approaches for effective fall risk prediction, which integrate standard functional tests with 3D skeleton data from depth sensors, are gaining increasing attention. However, the complex physiological and functional interactions among skeletal keypoints during ambulation pose challenges for multidimensional feature extraction in most predictive models. In this study, we developed a novel approach based on preprocessed 3D skeleton data, named Multivariate SpatialTemporal Gated Transformer (MSTG-Transformer). This approach consists of three main stages. First, gait cycle sequences are constructed to sophisticatedly depict the movement patterns of subjects, amplifying the distinctions between groups. Then, spatial and topological features are extracted via convolutional modules, and a dual-stream encoder block is employed to encode the features of 3D skeleton data across both time steps and time channels. Finally, a voting scheme is used to determine fall risk by integrating the classification results of individual gait cycle segments. Validation experiments on a real-world dataset demonstrate that our proposed approach outperforms classical methods, achieving a superior prediction accuracy of 0.9510 ± 0.0240. Additionally, our study highlights the crucial role of potential interactions between skeletal keypoints in accurately predicting fall risk.