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

• URL: http://arxiv.org/abs/2607.25710v1
• Date: Tue, 28 Jul 2026 13:33:57 GMT
• Title: Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data
• Abstract summary: Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern.<n>This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joints skeleton data treated as a graph.<n>Our approach enhances precision by distinguishing between false falls and actual impacts, contributing to better healthcare resource allocation.
• Score: 1.1654568410166084
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall Detection [1.1654568410166084]
  Falls among the elderly represent a significant public health concern due to their prevalence, consequences, and societal burden.<n>We propose DistillH-Mamba, a novel architecture for impact fall detection that addresses these challenges through three key innovations.<n>First, we introduce a hypergraph-based approach that captures higher-order relationships between multiple joints simultaneously.<n>Second, we integrate the Mamba architecture with hypergraphs for impact detection, significantly accelerating processing speed.<n>Third, we employ relational knowledge distillation that preserves crucial spatial-temporal relationships while reducing computational demands for real-time impact fall detection.
  arXiv  Detail & Related papers  (2026-07-03T09:56:57Z)
• An Improved 3D Skeletons UP-Fall Dataset: Enhancing Data Quality for Efficient Impact Fall Detection [1.806183113759115]
  The UP-Fall dataset, a key resource in fall detection research, has proven valuable but suffers from limitations in data accuracy and comprehensiveness.<n>This study presents enhancements to the UP-Fall dataset aiming at improving it for impact fall detection by incorporating 3D skeleton data.<n>Our preprocessing techniques ensure high data accuracy and comprehensiveness, enabling a more reliable impact fall detection.
  arXiv  Detail & Related papers  (2025-02-26T11:02:44Z)
• Epidemiology-informed Network for Robust Rumor Detection [59.89351792706995]
  We propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance.<n>To adapt epidemiology theory to rumor detection, it is expected that each users stance toward the source information will be annotated.<n>Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
  arXiv  Detail & Related papers  (2024-11-20T00:43:32Z)
• Computer-Aided Fall Recognition Using a Three-Stream Spatial-Temporal GCN Model with Adaptive Feature Aggregation [0.5235143203977018]
  Prevention of falls is paramount in modern healthcare, particularly for the elderly. A computer-aided fall detection system is inevitable to save elderly people's lives worldwide. This paper proposes a novel three-stream spatial-temporal feature-based fall detection system.
  arXiv  Detail & Related papers  (2024-08-22T08:40:04Z)
• GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured Data from Unauthorized Exploitation [61.80017550099027]
  Graph Neural Networks (GNNs) are increasingly prevalent in a variety of fields. Growing concerns have emerged regarding the unauthorized utilization of personal data. Recent studies have shown that imperceptible poisoning attacks are an effective method of protecting image data from such misuse. This paper introduces GraphCloak to safeguard against the unauthorized usage of graph data.
  arXiv  Detail & Related papers  (2023-10-11T00:50:55Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Context-Aware Change Detection With Semi-Supervised Learning [0.0]
  Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. Data sources like Sentinel-2 provide rich optical information, but are often hindered by cloud cover. We develop a model to assess the contribution of pre-disaster Sentinel-2 data in change detection tasks.
  arXiv  Detail & Related papers  (2023-06-15T08:17:49Z)
• Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks [7.624051346741515]
  An ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The proposed model achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively.
  arXiv  Detail & Related papers  (2023-04-13T08:30:46Z)
• Robust Trajectory Prediction against Adversarial Attacks [84.10405251683713]
  Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving systems. These methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks.
  arXiv  Detail & Related papers  (2022-07-29T22:35:05Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition [92.99291528676021]
  Instead of directly regressing the 3D joint locations, we decompose the task into bone direction prediction and bone length prediction. Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time. Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets.
  arXiv  Detail & Related papers  (2020-02-24T15:49:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.