Skeleton-based Fall Events Classification with Data Fusion

Leiyu Xie, Yuxing Yang, Fu Zeyu, Syed Mohsen Naqvi

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Human fall detection aims to classify falls and normal activities. It can improve the speed of rescue for the elderly after a fall occurs, it can also efficiently prevent the elderly from suffering secondary injuries due to untimely or inaccurate fall detection. This technology is widely used in hospitals, smart homes and nursing homes. The challenges are that the injured parts of the elderly can vary due to different types of fall events, such as falling sideways and falling backwards. In this paper, we propose a data fusion method which combine the skeleton keypoints captured from RGB images into fused keypoints to improve the performance of fall events classification. Four well known classification methods, Random Forest, Support Vector Machine, Multi-layer Perceptron, AdaBoost are used in the proposed framework. Meanwhile, the impact on fall detection results due to missing data caused by occlusion and or privacy protection is also analyzed through ablation study. The experimental results confirm that the proposed framework outperformed the state-of-the-art with reduced computational cost.

Original languageEnglish
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021 - Karlsruhe, Germany
Duration: 23 Sept 202125 Sept 2021

Conference

Conference2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021
Country/TerritoryGermany
CityKarlsruhe
Period23/09/2125/09/21

Keywords

  • Data Fusion
  • Fall Detection
  • Human Activity Recognition
  • Human Skeleton

Fingerprint

Dive into the research topics of 'Skeleton-based Fall Events Classification with Data Fusion'. Together they form a unique fingerprint.

Cite this