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

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 han

Categories

@misc{zorriassatine2026integrating,
  title = {{Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity}},
  author = {Farbod Zorriassatine and Ahmad Lotfi},
  year = {2026},
  month = apr,
  eprint = {2604.19538},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2604.19538}
}