ARXIV:2604.19538 · AGENTS · SUBMITTED 22 APR · 03:23 UTC · FRESHNESS STALE

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

Farbod Zorriassatine · Ahmad Lotfi · arXiv

A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention. Despite numerous approaches to fall mitigation through fall prediction and detection, existing systems have…

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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,…

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.

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Opportunity summary

Score3.0

PainA conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

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Unknown

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Competitive landscape

A conceptual framework integrating anomaly detection into agentic AI for proactive risk management in human activity, focusing on fall prevention.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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

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

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