Work

A skin-conformal liquid metal strain sensor integrating deep learning for neck motion monitoring

Year: 2026

Type: article

Abstract: Neck joints are susceptible to injury because of their extensive mobility and complex motion patterns, motivating the development of wearable motion sensors for health monitoring. Herein, we design a skin-conformal motion-monitoring strain sensor (SMS) based on a semi-liquid metal electrode and an adhesive encapsulation layer that promotes robust skin attachment. The sensor, fabricated by a simple printing method, exhibits consistent signal output across a broad range of stretching speeds, linear response up to 60% strain, detection of subtle 1% strain, and durability over 2,000 cycles without significant drift. Its strong adhesion also enables reusability and water resistance. When applied to multi-site neck motion recording and coupled with a convolutional neural network, the system classifies diverse neck movements with 96.9% accuracy. This work illustrates the promise of soft conductive materials and machine learning for practical neck healthcare applications.

Source: iScience

DOI: 10.1016/j.isci.2026.117142