Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity

TL;DR

This paper proposes a conceptual framework for integrating anomaly detection into agentic AI to proactively manage risks in human activities, such as fall prevention among the elderly.

Contribution

It introduces a novel perspective of using anomaly detection within agentic AI systems to improve risk management in complex, real-world human activity scenarios.

Findings

Formulates fall detection and prediction as anomaly detection problems.

Highlights the potential of agentic AI for early risk identification.

Proposes a dynamic, adaptive decision-making framework for risk management.

Abstract

Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risks in human activity, including the persistent hazard of falls among elderly populations. Despite numerous approaches to fall mitigation through fall prediction and detection, existing systems have not yet functioned as universal solutions across care pathways and safety-critical environments. This is largely due to limitations in consistently handling real-world complexity, particularly poor context awareness, high false alarm rates, environmental noise, and data scarcity. We argue that fall detection and fall prediction can usefully be formulated as anomaly detection problems and more effectively addressed through an agentic AI system. More broadly, this perspective enables the early identification of subtle deviations in movement patterns associated with increased risk, whether arising from age-related decline, fatigue, or environmental factors. While technical requirements for immediate deployment are beyond the scope of this paper, we propose a conceptual framework that highlights potential value. This framework promotes a well-orchestrated approach to risk management by dynamically selecting relevant tools and integrating them into adaptive decision-making workflows, rather than relying on static configurations tailored to narrowly defined scenarios.