ArticleACM transactions on intelligent systems and technology2021
Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.
Article in ACM transactions on intelligent systems and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 34 citations in OpenAlex.
- Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis.JMIR AI · 2026Article
- A Community-in-the-Loop Approach to Smart Home Monitoring for Aging in Place: Mixed Methods Evaluation of a Co-Designed Prototype.JMIR aging · 2026Article
- Smart home-assisted anomaly detection system for older adults: a deep learning approach with a comprehensive set of daily activities.Medical & biological engineering & computing · 2025Article
- Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids.Pain management nursing : official journal of the American Society of Pain Management Nurses · 2023Article
- Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series.International journal of nursing studies advances · 2022Article
- A Comprehensive "Real-World Constraints"-Aware Requirements Engineering Related Assessment and a Critical State-of-the-Art Review of the Monitoring of Humans in Bed.Sensors (Basel, Switzerland) · 2022Review
- Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences.Sensors (Basel, Switzerland) · 2021Article
- Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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 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.
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Registered trials
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