Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Ind...

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Veröffentlicht in:ACM transactions on intelligent systems and technology Jg. 12; H. 2; S. 1
Hauptverfasser: Dahmen, Jessamyn, Cook, Diane J
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2021
Schlagworte:
Human-centered computing→Ubiquitous and mobile computing
Computing methodologies→Machine learning algorithms
Applied computing→Life and medical sciences
Bayesian optimization
Anomaly detection
Smart homes
ISSN:2157-6904
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Zusammenfassung:Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly-Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly-supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically-relevant behavior anomalies from over 2 million sensor readings collected in 5 smart homes, reflecting 26 health events. Results indicate that indirectly-supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2157-6904
DOI:10.1145/3439870