Privacy-Preserving Fall Detection in Healthcare using Shape and Motion Features from Low-Resolution RGB-D Videos
Paper i proceeding, 2016

This paper addresses the issue on fall detection in healthcare using RGB-D videos. Privacy is often a major concern in video-based detection and analysis methods. We propose a video-based fall detection scheme with privacy preserving awareness. First, a set of features is defined and extracted, including local shape and shape dynamic features from object contours in depth video frames, and global appearance and motion features from HOG and HOGOF in RGB video frames. A sequence of time-dependent features is then formed by a sliding window averaging of features along the temporal direction, and use this as the input of a SVM classifier for fall detection. Separate tests were conducted on a large dataset for examining the fall detection performance with privacy-preserving awareness. These include testing the fall detection scheme that solely uses depth videos, solely uses RGB videos in different resolution, as well as the influence of individual features and feature fusion to the detection performance. Our test results show that both the dynamic shape features from depth videos and motion (HOGOF) features from low- resolution RGB videos may preserve the privacy meanwhile yield good performance (91.88% and 97.5% detection, with false alarm ≤ 1.25 %). Further, our results show that the proposed scheme is able to discriminate highly confused classes of activities (falling versus lying down) with excellent performance. Our study indicates that methods based on depth or low-resolution RGB videos may still provide effective technologies for the healthcare, without impact personnel privacy.

RGB-D videos

Privacy-preserving video analysis

Motion feature

HOG of Optical Flow

Fall detection

Healthcare

Dynamic shape feature

Författare

Irene Yu-Hua Gu

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Durga Priya Kumar

Chalmers, Signaler och system

Yixiao Yun

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 9730 490-499

Ämneskategorier

Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi

Signalbehandling

Datorseende och robotik (autonoma system)

Styrkeområden

Informations- och kommunikationsteknik

Transport

Livsvetenskaper och teknik

DOI

10.1007/978-3-319-41501-7_55

ISBN

978-3-319-41501-7