Objective: This study examines digital transformation trends in ergonomics with a focus on the transition from human-state monitoring to human state-based resource allocation and proposes an integrated framework.

Background: Digital transformation has expanded ergonomics from physical-fit design to data-driven, human-state adaptive systems. However, the connection between human-state evidence and resource-allocation decisions remains underdeveloped.

Method: A horizon scanning and strict code-based review were conducted using IEA, ESK/JESK, and JES/JSK records from 2019 to 2025. A total of 3,770 raw records were identified. After duplicate removal, period filtering, eligibility screening, and manual-review separation, 1,317 strict-coded records were included.

Results: Human State Sensing was the largest substantive category excluding the umbrella application-domain category (B, n=343, 26.04%). Cognitive Workload and Attention (C, n=171, 12.98%) and Fatigue, Stress, and Recovery (D, n=130, 9.87%) followed. Resource Allocation Decisions (F, n=21, 1.59%) and Governance and Ethics (G, n=29, 2.20%) remained limited.

Conclusion: The findings identify an evidence-to-decision gap: human-state sensing and application-domain studies are more prevalent than studies that translate state information into allocation decisions and governance mechanisms. A five-step framework consisting of Sensing, Modeling, Allocation, Feedback, and Governance is proposed.

Application: The framework can guide fatigue management, adaptive task assignment, worker-participatory feedback, and governance design in industrial, educational, healthcare, and transportation settings.