An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment
文献类型：期刊论文
| 作者 | Yu, Miao1 ; Yu, Yuanzhang2; Rhuma, Adel1; Naqvi, Syed Mohsen Raza1; Wang, Liang3; Chambers, Jonathon A.1
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| 出版日期 | 2013-11-01 |
| 卷号 | 17期号:6页码:1002-1014 |
| 关键词 | Assistive living fall detection health care online one class support vector machine (OCSVM) posture detection |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics |
| 研究领域[WOS] | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 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记录号 | WOS:000327394200004 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3782]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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