An online one class support vector machine based.pdf (9.67 MB)
Download file

An online one class support vector machine based person-specific fall detection system for monitoring an elderly individual in a room environment

Download (9.67 MB)
journal contribution
posted on 23.08.2013, 13:17 authored by Miao Yu, Yuanzhang Yu, Adel Rhuma, Mohsen Naqvi, Liang Wang, Jonathon Chambers
In this paper, we propose a novel computer vision based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person’s daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on data sets for 12 people, we confirm that our proposed person specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semi-unsupervised fall detection system from a system perspective because although an unsupervised type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step towards a complete unsupervised fall detection system.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

YU, M. ... et al., 2013. An online one class support vector machine based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE Journal of Biomedical and Health Informatics, 17 (6), pp. 1002-1014.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2013

Notes

This article was published in the IEEE Journal of Biomedical and Health Informatics [© IEEE] and the definitive version is available at: http://dx.doi.org/10.1109/JBHI.2013.2274479 Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Language

en