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Enhancing Privacy with Optical Element Design for Fall Detection

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posted on 2025-01-23, 13:12 authored by Liyun GongLiyun Gong, Sheldon McCallSheldon McCall, Miao YuMiao Yu

Falling poses significant risks, especially for the geriatric population. In this study, we introduce an innovative approach to privacy-preserving fall detection using computer vision. Our technique leverages a deep neural network (DNN) to accurately identify falling events in input images, while simultaneously prioritizing privacy through the implementation of an optical element. The experimental results establish that our proposed method outperforms alternative hardware and software-based privacy-preserving approaches in terms of encryption level and accuracy. These results are derived from an extensive dataset encompassing diverse falling scenarios.

History

School affiliated with

  • School of Computer Science (Research Outputs)
  • College of Health and Science (Research Outputs)
  • School of Engineering and Physical Sciences (Research Outputs)

Publication Title

Enhancing Privacy with Optical Element Design for Fall Detection

Volume

59

Issue

20

Pages/Article Number

e12995

Publisher

Electronic Letters

ISSN

0013-5194

eISSN

1350-911X

Date Accepted

2023-09-06

Date of First Publication

2023-10-21

Date of Final Publication

2023-10-21

Open Access Status

  • Open Access

Will your conference paper be published in proceedings?

  • N/A

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