Fuzzy Fault Detection and Band-Conditioned Data Quality Assessment for Sensor Streams: A Continuous Glucose Monitoring Case Study

Sensor faults can propagate beyond raw measurements and distort the aggregates used to interpret a monitored phenomenon. We propose a domain-agnostic, five-layer fuzzy pipeline that characterizes the reliability of a sensor stream jointly over time and value bands and carries it through to every derived aggregate: (L1) fault tagging of each sample against five fuzzy failure primitives; (L2) fuzzy windowing; (L3) band reliability; (L4) routed repair with propagated reconstruction uncertainty; and (L5) veracity aggregation with linguistic protoforms. An end-to-end acquisition architecture is also specified. The pipeline was instantiated for continuous glucose monitoring (CGM) with clinical knowledge of glucose dynamics and applied to a 14-month recording of an adult with type 1 diabetes (FreeStyle Libre 3, 5-min cadence), of which 294 days contained at least 120 readings. Clean reference days remained essentially unchanged (mean sample quality 0.98-0.99). On the most corrupted day, the estimated time below range fell from 16% to 3%, with a veracity of only 0.14. Over the recording, 38% of the days were too unreliable to phenotype and 5% changed phenotype. Joint time- and band-conditioned reliability assessment thus prevents corrupted sensor data from producing falsely precise clinical conclusions.