Data-Driven Ergonomic Load Dynamics for Human–Autonomy Teams

Ergonomic load in human–autonomy teams is commonly treated as a static score or a post-hoc audit, even though modern sensing and communication enable real-time regulation of operator effort. We model ergonomic load as a dissipative dynamical state inferred online from multimodal effort proxies and task context, and couple it to autonomy through load-dependent gain moderation and compliance shaping. The method is evaluated on public human–swarm and human–robot interaction traces together with effort-proximal wearable and myographic datasets using a unified, windowed pipeline and controlled stress tests that emulate latency, downsampling, packet loss, and channel dropouts. On a large human–swarm benchmark, the estimator achieves strong discrimination and calibration for rare high-load events (up to AUROC 0.87, AUPRC 0.41, ECE 0.031 at q=0.90) and degrades predictably under delay, with a knee around 300–400ms (AUROC 0.87→0.80, ECE 0.031→0.061 at 500ms). Embedding the estimate in the adaptation schedule reduces overload incidence and oscillatory redistribution while preserving coordination proxies in surrogate closed-loop simulation: overload time drops from 7.8% to 4.1% (relative reduction ≈ 47%) with throughput maintained near baseline (1.00→0.97) and oscillation power reduced (0.26→0.14) under nominal timing. These results provide a reproducible pathway for making ergonomics a control-relevant feedback signal, together with explicit operational constraints on estimator calibration (target ECE ≤0.05) and end-to-end latency (effective τ≤300ms) required to avoid regime switching and maintain stable, interpretable adaptation.