Enhancing privacy with optical element design for fall detection

Abstract

Falling poses significant risks, especially for the geriatric population. In this study, the authors introduce an innovative approach to privacy‐preserving fall detection using computer vision. The authors’ 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 the authors’ 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.

Computer Vision Based Transfer Learning-Aided Transformer Model for Fall Detection and Prediction

“…Effectively addressing these concerns necessitates the adoption of encryption techniques. One approach involves employing both software [43] and hardware [44] based encryption methods, which can be applied to blur images within the captured videos, thereby safeguarding privacy. Furthermore, encryption techniques such as SSL (Secure Sockets Layer) and TLS (Transport Layer Security) [45] play a pivotal role in enhancing privacy during the data transmission process to the cloud server.…”