A spatio-temporal graph neural network for fall prediction with inertial sensors

Shu Wang, Xiaohu Li, Guorui Liao, Jiawei Liu, Changbo Liao, Ming Liu, Jun Liao, Li Liu

Abstract: Falls are the leading cause of unintentional human injury, having become a public health event of strong social concern. The fall prediction technology based on wearable inertial sensors is a relatively reliable solution in human activity monitoring, a user scenario with mobility and high information privacy sensitivity, and has the advantages of low cost, small size, and high precision. However, a key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to represent human low limbs information and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.