Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity
Farbod Zorriassatine, Ahmad Lotfi
TLDR
This paper proposes integrating anomaly detection into agentic AI to proactively manage human activity risks, like falls, by identifying subtle movement deviations.
Key contributions
- Existing fall mitigation systems struggle with real-world complexity, poor context awareness, and high false alarms.
- Proposes framing fall detection and prediction as anomaly detection problems.
- Integrates anomaly detection into agentic AI for proactive risk management in human activity.
- Introduces a conceptual framework for adaptive, dynamic risk management workflows.
Why it matters
This paper offers a novel perspective by reframing fall prediction as an anomaly detection problem within agentic AI. This approach promises more robust, context-aware risk management, potentially reducing falls and improving safety for vulnerable populations. It moves beyond static solutions.
Original 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.
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