Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data

2026-07-28

AI Summary

This study addresses the challenge of distinguishing genuine fall impacts from non-impact balance losses in fall monitoring. To this end, the authors propose a spatiotemporal graph modeling approach based on 3D skeletal data. By constructing a spatiotemporal graph of human joints and leveraging a Spatial-Temporal Graph Convolutional Network (STGCN) to extract spatial-temporal features, the method further integrates GRU and BiLSTM modules to enhance temporal dynamics modeling for precise identification of fall impact moments. This work presents the first integration of STGCN with bidirectional recurrent neural networks for fall detection, achieving over 90% accuracy on an enhanced version of the UP-Fall dataset. The proposed approach significantly improves the discrimination between true and false falls, and the refined dataset is publicly released to support future research in this domain.

Abstract

Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joints skeleton data treated as a graph using Spatio-Temporal Graph Convolutional Networks (STGCN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (BiLSTM) layers. By pinpointing impact moments, our approach enhances precision by distinguishing between false falls and actual impacts, contributing to better healthcare resource allocation. Our methodology, evaluated using the improved 3D skeletons UP-Fall dataset, achieves accuracy exceeding 90\% across various fall scenarios. We have made this improved dataset publicly available at https://zenodo.org/records/12773013 to facilitate further research.

Problem

• impact detection
• fall events
• 3D skeleton data
• elderly health
• false fall

Innovation

• Spatio-Temporal Graph Convolutional Networks
• Impact Detection
• 3D Skeleton Data
• Fall Detection
• Recurrent Neural Networks