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

Author:

Dahmen Jessamyn1,Cook Diane J.1

Affiliation:

1. Washington State University, Pullman, WA

Abstract

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 2M sensor readings collected in five 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.

Funder

National Institutes of Health

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

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3. Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification;ACM Transactions on Intelligent Systems and Technology;2024-01-16

4. Anomaly Detection in Smart Environments: A Comprehensive Survey;IEEE Access;2024

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