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

Author

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Durga Priya Kumar

Chalmers, Signals and Systems

Yixiao Yun

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 9730 490-499

Subject Categories

Health Care Service and Management, Health Policy and Services and Health Economy

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Information and Communication Technology

Transport

Life Science Engineering (2010-2018)

DOI

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

ISBN

978-3-319-41501-7

More information

Created

10/8/2017