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

2026-04-21

AI Summary

Existing fall prediction and detection systems often suffer from insufficient contextual awareness, high false alarm rates, susceptibility to environmental noise, and data scarcity in real-world settings, limiting their generalizability for proactive risk mitigation. This work proposes a novel, goal-oriented active risk预警 framework that integrates anomaly detection with agent-based AI to enable autonomous decision-making. By dynamically selecting tools and adapting its decision pipeline, the framework incorporates contextual awareness and anomaly detection algorithms, replacing conventional static, handcrafted solutions. Experimental results demonstrate that the approach effectively captures subtle deviations in movement patterns within high-risk scenarios—such as falls among older adults—significantly enhancing both generalization and foresight. This study lays a theoretical foundation for developing universal, proactive human activity risk management systems.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Anomaly Detection
• Agentic AI
• Fall Detection
• Fall Prediction
• Risk Management

Innovation

• Agentic AI
• anomaly detection
• fall prediction
• proactive risk management
• context-aware decision-making