A Safety-Governed Architecture for Adaptive Sequential Sensing in Robot-Assisted Rehabilitation

Diagnostic uncertainty in neurological rehabilitation motivates robotic systems that can select informative sensing actions adaptively rather than rely on fixed assessment protocols or static clinical records. This work introduces the Embodied Evidence Acquisition and Reasoning Loop (EARL), a typed architecture in which five role-specialized critics propose and assess sequential sensing actions, a probabilistic simulation sandbox estimates their expected information value, and a deterministic safety governor retains exclusive execution authority. Observed, derived, and simulated evidence remain provenance-distinct in a replayable, hash-linked ledger. EARL was evaluated retrospectively on 64 subjects from the PhysioNet Gait in Neurodegenerative Disease Database using repeated subject-level cross-validation, 11 predeclared conditions, and 3520 replay-verified runs. The primary endpoint was area under cumulative posterior-entropy reduction. EARL achieved 7.689 (95% CI 7.535–7.840), exceeding fixed-order and random selection by 0.459 and 0.763, respectively; the EARL-minus-EIG difference was -0.321. EARL nevertheless achieved higher macro accuracy (55.8% versus 49.5%) and a lower Brier score (0.781 versus 0.813) than pure expected-information-gain selection, demonstrating a trade-off among uncertainty reduction, discrimination, calibration, and safety-constrained decision making. All 10 deterministic safety-conformance scenarios produced their expected outcomes. These results establish reproducible software behavior for bounded sequential retrospective sensing; they do not establish clinical diagnostic performance, treatment benefit, physical-robot safety, or patient efficacy.