Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors

Xiaohu Li; Jiawei Liu; Guorui Liao; Mingrui Yin; Shu Wang; Guoxin Su; Jun Liao; Li Liu

ICASSP 2024accepted0 citations

Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors

Xiaohu Li, Jiawei Liu, Guorui Liao, Mingrui Yin, Shu Wang, Guoxin Su, Jun Liao, Li Liu

Abstract

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 capture 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.

BibTeX

@inproceedings{icassp2024_predictingfallev,
  title = {Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors},
  author = {Xiaohu Li and Jiawei Liu and Guorui Liao and Mingrui Yin and Shu Wang and Guoxin Su and Jun Liao and Li Liu},
  booktitle = {ICASSP 2024},
  year = {2024}
}