Eliminating Environmental Context for Fall Detection Based on Movement Traces

J. Balamanikandan, Senthil Kumar Thangavel, Maiga Chang

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

Abstract

Falls are a prominent cause of mortality and severe injury among the elderly. It can be prevented by tracking them and providing prompt treatment. Current fall detection systems use data from sensors or cameras in various ways. False positives are common in sensor-based systems, and operating system constraints make privacy a major concern in vision-based systems. This paper proposes a technique for detecting falls from RGB images using a convolutional neural network (CNN) utilizing movement trace characteristics generated by a modified structural similarity index (SSIM) that can be integrated into resource-constrained devices for in-house monitoring. The proposed approach uses a camera system and is tested against the UR Fall detection (URFD) dataset, outperforming previous fall detection systems. Our method achieves 99% accuracy. The model's dependence on readily available sensors and superior performance on the URFD dataset makes it a viable option for reliable fall detection in the real world.

Original languageEnglish
Title of host publicationSoft Computing and Signal Processing - Proceedings of 5th ICSCSP 2022
EditorsV. Sivakumar Reddy, V. Kamakshi Prasad, Jiacun Wang, K.T.V. Reddy
Pages343-357
Number of pages15
DOIs
Publication statusPublished - 2023
Event5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022 - Hyderabad, India
Duration: 24 Jun. 202225 Jun. 2022

Publication series

NameSmart Innovation, Systems and Technologies
Volume313
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022
Country/TerritoryIndia
CityHyderabad
Period24/06/2225/06/22

Keywords

  • Computer vision
  • Convolutional neural networks
  • Environmental context
  • Fall detection
  • Healthcare
  • Movement tracking
  • Pattern recognition

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