A geometry-aware and customizable multimodal sensing system for texture and material identification in prosthetics

The restoration of natural somatosensation remains largely absent in upper-limb prostheses. While many current electronic skins target to replicate human tactile sensing, they are still limited by coarse resolution, planar form factors, and high fabrication cost. Here, we present a geometry-aware and customizable multimodal sensing system for prosthetics, functioning as a sensing component for mimicking human skin. The system integrates high-resolution pressure and temperature mapping in conformal, free-form modules. A scan-model-print workflow converts a patient’s prosthesis topography into 3D sensor curvature for seamless surface conformity. Self-aligning snap-fit interconnects enable on-demand reconfiguration and routing across sensor modules, while interlaced pressure and temperature matrices maximize spatial utilization. Compared with commercial glove sensors, the system achieves ~10-fold finer spatial resolution for pressure and denser temperature mapping. A multi-head neural network performs real-time calibration and denoising, reducing per-pixel error and improving signal fidelity. With high-rate pressure sampling and sensitive temperature readout, the sensing system enables accurate texture and material recognition. This sensing system serves as a bridge that brings natural tactile sensing on artificial prostheses, providing a platform for future development of a full bionic skin with both sensing and haptic stimulation functions for personalized neuroprosthetics.