Sensitivity‐Enhanced Skin‐Attachable Strain Sensors Using Multi‐Material Printed Bioinspired Heterogeneous Moduli Structures for Proprioceptive Training

Author:

Li Yuanlong1,Lin Rongzan1,Wang Xianheng2,Li Haojie1,Chen Yuqiu1,Zhang Boxuan1,Zhang Yanlin3,Pan Yu3,Qiu Xinming2,Liu Ran1ORCID

Affiliation:

1. Department of Biomedical Engineering School of Medicine Tsinghua University Beijing 100084 China

2. Department of Engineering Mechanics School of Aerospace Engineering Tsinghua University Beijing 100084 China

3. Department of Rehabilitation Medicine School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 100084 China

Abstract

AbstractProprioception evaluated by the repetition performance of limbs’ movement is an important indicator of sports rehabilitation. Since motion monitoring using wearable sensors is hampered by challenges in sensitivity, conformability, and stability, proprioception assessments still rely heavily on bulky apparatus. This work reports a bioinspired strain sensor with sensitivity‐enhanced structures based on strain redistribution to achieve proprioceptive training of joints, showing high sensitivity and stability even under large deformation. The sensing structure consists of units with different moduli and is independent of the sensing materials and sensors’ shape, and thus can be universally applied to various devices. A new method of multi‐material 3D printing is developed to construct the complex substrates with heterogeneous moduli architecture and automatically fabricate the entire sensor. To fulfill effective proprioception exercise, the conformal deformation of printed sensors attached to joints during the whole stretch period is verified in computed tomography imaging, which indicates the output of sensors can precisely reflect the movement of joints. A complete recovery cycle of 4 weeks is demonstrated, during which an injured patient training with our skin‐attachable strain sensors restored his ability in limb control. This proprioceptive training paradigm with intelligent sensors paves the way to home‐based rehabilitation.

Funder

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Publisher

Wiley

Subject

Industrial and Manufacturing Engineering,Mechanics of Materials,General Materials Science

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