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- Publisher Website: 10.1109/JIOT.2020.3019270
- Scopus: eid_2-s2.0-85100806799
- WOS: WOS:000616317000039
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Article: A Nonintrusive Elderly Home Monitoring System
Title | A Nonintrusive Elderly Home Monitoring System |
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Authors | |
Keywords | Anomaly detection blockchain home monitoring IoT |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER288-ELE |
Citation | IEEE Internet of Things Journal, 2021, v. 8 n. 4, p. 2603-2614 How to Cite? |
Abstract | Home anomaly monitoring is crucial for the elderly who live alone. A number of IoT-based home monitoring systems have been available, but most rely on privacy-intrusive cameras. With more and more concerns on privacy and security of human data, anomaly detection based on nonintrusive IoT devices becomes more desirable. Considering the elderly consumers, a low-cost system with good detection accuracy is further critical for the system's acceptability by elderly users. We propose a smart home monitoring system for living-alone senior citizens, relying on carefully designed, low-cost infrared sensor devices, as well as a cloud-based data processing and anomaly detection platform. Our PIR sensor device is effective in continuous monitoring of motion data in a user's apartment, and an open-hardware software platform is devised to support sensors manufactured by various vendors in the IoT system, all for cost reduction purpose. For privacy preservation, we encrypt collected data and store data indices in a blockchain system, to achieve efficient data access control and auditing. For motion anomaly detection, we propose a simple but effective environment adaptation method to work with the one-class support vector machine (OCSVM) method. Experiments driven by real-world traces show good reliability, accuracy, and efficiency of our system. |
Persistent Identifier | http://hdl.handle.net/10722/301333 |
ISSN | 2023 Impact Factor: 8.2 2023 SCImago Journal Rankings: 3.382 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | FANG, L | - |
dc.contributor.author | Wu, Y | - |
dc.contributor.author | Wu, C | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2021-07-27T08:09:33Z | - |
dc.date.available | 2021-07-27T08:09:33Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Internet of Things Journal, 2021, v. 8 n. 4, p. 2603-2614 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301333 | - |
dc.description.abstract | Home anomaly monitoring is crucial for the elderly who live alone. A number of IoT-based home monitoring systems have been available, but most rely on privacy-intrusive cameras. With more and more concerns on privacy and security of human data, anomaly detection based on nonintrusive IoT devices becomes more desirable. Considering the elderly consumers, a low-cost system with good detection accuracy is further critical for the system's acceptability by elderly users. We propose a smart home monitoring system for living-alone senior citizens, relying on carefully designed, low-cost infrared sensor devices, as well as a cloud-based data processing and anomaly detection platform. Our PIR sensor device is effective in continuous monitoring of motion data in a user's apartment, and an open-hardware software platform is devised to support sensors manufactured by various vendors in the IoT system, all for cost reduction purpose. For privacy preservation, we encrypt collected data and store data indices in a blockchain system, to achieve efficient data access control and auditing. For motion anomaly detection, we propose a simple but effective environment adaptation method to work with the one-class support vector machine (OCSVM) method. Experiments driven by real-world traces show good reliability, accuracy, and efficiency of our system. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER288-ELE | - |
dc.relation.ispartof | IEEE Internet of Things Journal | - |
dc.rights | IEEE Internet of Things Journal. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. | - |
dc.subject | Anomaly detection | - |
dc.subject | blockchain | - |
dc.subject | home monitoring | - |
dc.subject | IoT | - |
dc.title | A Nonintrusive Elderly Home Monitoring System | - |
dc.type | Article | - |
dc.identifier.email | Wu, C: cwu@cs.hku.hk | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Wu, C=rp01397 | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JIOT.2020.3019270 | - |
dc.identifier.scopus | eid_2-s2.0-85100806799 | - |
dc.identifier.hkuros | 323505 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 2603 | - |
dc.identifier.epage | 2614 | - |
dc.identifier.isi | WOS:000616317000039 | - |
dc.publisher.place | United States | - |