Privacy-Preserving In-Home Fall Detection Using Visual Shielding Sensing and Private Information-Embedding

Jixin LIU, Rong TAN, Guang HAN, Ning SUN, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)


Falls are the main cause of accidental injuries, and even death among elderly people, especially those who live alone in their homes. The absence of a reliable fall detection system has long been a serious problem for home health monitoring. A video surveillance system can be used to monitor elderly people at home to detect falls, but the traditional implementation of such intelligent detection falls short of personal privacy-related considerations; additionally, many people do not want to be watched in their homes. To solve this problem, we propose a fall detection system with visual shielding that can ensure the safety of elderly people in their homes while preserving their personal privacy. Multilayer compressed sensing is first used to achieve visually shielded video frames. By combining low-rank sparse decomposition theory with the improved local binary pattern on the three orthogonal planes, the object features are extracted from the shielded video frames. Finally, to compensate for the information lost in the compressed video to a certain extent, a private information-embedded classification model is proposed to identify fall-related behavior. The experimental results on two public fall datasets show that the proposed method delivers impressive accuracy and a low error rate while effectively distinguishing between fall- and nonfall-related behaviors in videos.
Original languageEnglish
Pages (from-to)3684-3699
JournalIEEE Transactions on Multimedia
Early online date12 Oct 2020
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

This work was supported by funds from the Provincial Natural Science Foundation of the Science and Technology Bureau of Jiangsu Province under Grant BK20180088, the China Postdoctoral Science Foundation under Grant 2019M651916, the Scientific Research Foundation of Nanjing University of Posts and Telecommunications under Grant NY218066 and the Natural Science Foundation of China under Grant 61871445.


  • Fall detection
  • information embedded
  • multilayer compressed sensing
  • privacy-preserving


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