Work

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

Year: 2026

Type: article

Abstract: The restoration of natural somatosensation remains largely absent in upper-limb prostheses. Current electronic skins 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 prostheses that functions as a sensing component for mimicking human skin. The system integrates high-resolution and high-rate 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, 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 temperature. A multihead neural network performs real-time calibration and denoising, thereby reducing per-pixel error and improving signal fidelity. The system enables texture and material identification and provides a sensing platform toward personalized prostheses with integrated sensing and haptic feedback.

Source: Cell Reports Physical Science

DOI: 10.1016/j.xcrp.2026.103458

Authors: Hongyi Shen, Nikolai Bogdev, Yusen Zhang, Shanshan Yao, Prashanta Dutta, Kaiyan Qiu

Institutions: Washington State University, Stony Brook University

Language: English

Topic: Advanced Sensor and Energy Harvesting Materials

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,620