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An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment
Yu, Miao1; Yu, Yuanzhang2; Rhuma, Adel1; Naqvi, Syed Mohsen Raza1; Wang, Liang3; Chambers, Jonathon A.1
发表期刊IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
2013-11-01
卷号17期号:6页码:1002-1014
文章类型Article
摘要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 datasets 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 semiunsupervised 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 toward a complete unsupervised fall detection system.
关键词Assistive Living Fall Detection Health Care Online One Class Support Vector Machine (Ocsvm) Posture Detection
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
关键词[WOS]REAL-TIME ; HOME-ENVIRONMENT ; HUMAN-BODY ; IMPLEMENTATION ; CLASSIFICATION ; SEGMENTATION ; SURVEILLANCE ; RECOGNITION
收录类别SCI
语种英语
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号WOS:000327394200004
引用统计
被引频次:100[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/3782
专题智能感知与计算
作者单位1.Univ Loughborough, Sch Elect Elect & Syst Engn, Loughborough LE11 3TU, Leics, England
2.Shandong Univ Technol, Zibo 255049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714
Yu, Miao,Yu, Yuanzhang,Rhuma, Adel,et al. An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2013,17(6):1002-1014.
APA Yu, Miao,Yu, Yuanzhang,Rhuma, Adel,Naqvi, Syed Mohsen Raza,Wang, Liang,&Chambers, Jonathon A..(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),1002-1014.
MLA Yu, Miao,et al."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(2013):1002-1014.
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