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Research on the Application of Fall Detection Technology Based on the Security Field of the Smart Care for the Elderly

Published: 15 April 2022 Publication History

Abstract

Abstract. The security of the elderly in an aging society has become a social problem attracting much attention, among which the problem of secondary injury caused by the fall has attracted wide concern of researchers. The application of fall detection combined with artificial intelligence technology has become an important application in the security field of smart care for the elderly. After analyzing and judging the human body features, this paper found out that the combination of human skeleton and fall detection technology could be used to identify the fall behavior and link it with the alarm system to solve the security problem of smart care for the elderly. The experimental results showed that the recognition rate of fall detection was increased by 2% after adding optimization of genetic algorithm factors and contrast calculation of prediction frame length in the process of human feature target detection.

References

[1]
National Bureau of Statistics Bulletin of the Seventh National Census No 8.
[2]
Ye L A, Qi Y Z, Shi J X, Guan Q J. 2020. Review of research on human fall detection technology Electronic Test, vol 2, pp. 50–51+65.
[3]
Chen Y B, He H W, Wang G Z, Wang G T. 2019. The fall detection system for the elderly based on machine vision Automation & Information Engineering, vol 5, pp. 37–41.
[4]
Xu C, Cheng W X, Yang Y Z. 2020. Research on the Basis of Yolo Target Detection Algorithm Computer and Information Technology, vol 4, pp. 45–47.
[5]
Xu Y, Yuan H W, Li Z. 2018. Research on Handwritten Digital Recognition Based on CNN and TensorFlow Shanghai Electric Technology vol 1, pp. 31–34+61.
[6]
Shi L, Jing M G, Fan Y B, Zeng X Y. 2020. Segmentation Detection Algorithm Based on R-CNN Algorithm Journal of Fudan University (Natural Science), vol 4, pp. 412–418.
[7]
Yang X Q, Tang X, Zhang G B, Wing M W. 2019. Human fall detection method based on YOLO network Journal of Yangzhou University (Natural Science Edition), vol 2, pp. 61–64+78.

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AIEE '22: Proceedings of the 2022 3rd International Conference on Artificial Intelligence in Electronics Engineering
January 2022
149 pages
ISBN:9781450395489
DOI:10.1145/3512826
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 April 2022

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Author Tags

  1. Fall Detection
  2. Genetic Algorithm
  3. Human Features
  4. Keywords. Smart Care for the Elderly

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