This article introduces an adhesive interposer-based reconfigurable multi-sensor patch interface with on-chip quantized time-domain feature extraction, tailored for heterogeneous physiological and environmental monitoring. A proposed patch concept includes integrated micro-scale structures for comfortable pressure-based reconfiguration, allowing easy attachment and detachment of various sensor elements. For embedding edge-computing capability into a miniaturized patch device with a conventional legacy microcontroller, its multi-sensor interface integrated circuit (IC) is proposed to include on-chip analog feature extraction and classification engines of quantized time-domain convolutional neural network (QTD-CNN) and one-shot computing binary neural network (BNN). The design employs 1-bit past-data quantization, analog normalization, and flexible time window schemes to minimize leakage problems in conventional analog engines and supports reconfigurable operations for healthcare and environmental applications. This interface IC includes five types of readout frontends for chemo-resistive sensors, electrochemical sensors, biopotentials, bioimpedance, and photoplethysmogram, where every path is designed to provide both wide dynamic range (DR) and compensation capability of gain and offset. A system-level prototype of the adhesive interposer-based reconfigurable patch interface has been developed, demonstrating its effectiveness in gas event and arrhythmia detection with hit rates of 91% and 92%, respectively.