Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review

Clinical Snapshot

Key Findings

• P Value: Not applicable - narrative review

• Effect Size: Not applicable - narrative review

• Primary Outcome: Identification of three primary motivations for anomaly detection: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy

• Nnt Or Sensitivity: Technologies show promise for real-time monitoring but require improved accuracy and reduced false positives for clinical effectiveness

• Confidence Interval: Not applicable - narrative review

Clinical Application

Variable depending on technology type, user compliance, and technical infrastructure; requires consideration of digital literacy and acceptance Highly relevant to Australian aged care policy emphasizing aging in place; potential integration with NDIS supports and telehealth initiatives under Medicare People with dementia living at home and their family caregivers, particularly relevant for mild to moderate dementia stages

Abstract

Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.

References

1. 1.Lai, J., Ye, B., & Mihailidis, A. (2026). Anomaly detection technologies for dementia care: Monitoring goals, sensor applications, and trade-offs in home-based solutions-A narrative review. Journal of Applied Gerontology. https://doi.org/10.2217/nmt-2015-0007