FIID: Feature-Based Implicit Irregularity Detection Using Unsupervised Learning From IoT Data for Homecare of Elderly

Advances in wireless sensor networks and increasing Internet-of-Things devices give great opportunities for smart homecare of the elderly. Smart homecare has been a promising issue and received much attention recently. Irregularity detection is one of the most important issues in smart homecare for...

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Bibliographic Details
Published in:IEEE internet of things journal 2020-11, Vol.7 (11), p.10884-10896
Main Authors: Shang, Cuijuan, Chang, Chih-Yung, Liu, Jinjun, Zhao, Shenghui, Sinha Roy, Diptendu
Format: Article
Language:English
Subjects:
Biomedical monitoring
Daily behavior
Data mining
Feature extraction
Hidden Markov models
homecare
Internet of Things
Irregularities
irregularity detection
Monitoring
Older people
Regularity
Senior citizens
Subjective assessment
Unsupervised learning
Wireless sensor networks
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Summary:Advances in wireless sensor networks and increasing Internet-of-Things devices give great opportunities for smart homecare of the elderly. Smart homecare has been a promising issue and received much attention recently. Irregularity detection is one of the most important issues in smart homecare for assessing the health condition of the elderly. However, most of the researches focused on the explicit irregularity detections which are usually based on the drastic changes of sensor data, such as falling. Existing mechanisms for detecting implicit irregularity rely on the subjective assessment of behaviors' importance by the elder and simply outputs the binary detection results. This article proposes a feature-based implicit irregularity detection mechanism (FIID), which extracts the regularity features using unsupervised learning and outputs the probability of implicit irregularity. The proposed FIID identifies the regular behaviors which satisfy the time-regular and happen-frequently properties as the regularity features of daily behaviors. These features then construct a multidimensional feature space to calculate the implicit irregularity probability of the daily health condition. Performance results show that the proposed FIID outperforms the existing implicit irregularity mechanism in terms of precision, recall as well as F-measure.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.2990556