CogCBR: A Complete Case-Based Reasoning Framework for Wearable-Sensor-Based Gait Screening of Neurodegenerative Diseases

Wearable force-sensitive insoles enable quantitative gait analysis as a screening aid for neurodegenerative diseases (NDDs), yet prevailing machine learning pipelines give point predictions with no per-case reliability estimate, no intrinsic explanation, and no way to curate their own knowledge base. Case-Based Reasoning (CBR) mirrors clinical reasoning, but deployed healthcare CBR systems typically implement only partial R4 cycles, omitting Revise and Retain. We propose CogCBR, a sensor-driven framework that operationalizes the complete R4 cycle—Retrieve, Reuse, Revise, Retain—for gait-based NDD screening within Richter’s four knowledge containers, pairing weighted case retrieval with confidence-based clinical triage and a label-verified case-base maintenance policy. On the PhysioNet GaitPDB cohort, CogCBR attains an AUC of 0.861—statistically on par with the strongest tuned baseline under matched tuning, yet the only method evaluated that also provides confidence-based triage, case-based explanation, and longitudinal case-base maintenance, the last validated in a deployment-style streaming simulation. An independent-cohort evaluation on GaitNDD yields an AUC of 0.902; under a stricter cross-modality transfer, however, CogCBR does not exceed the strongest classical baseline, which is also reported. With sub-millisecond inference and a compact footprint, CogCBR suits resource-constrained wearable and edge-health platforms. Prospective longitudinal clinical evaluation and validation in pre-clinical cohorts are left as future work.