PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 28, 2026Advanced Functional Materials0 citations

Bionic Hydrogel Sensor Patches for Embodied Human‐Machine Interaction: Low‐Constraint Wearable Design and Applications

View Full Paper
YLYiyun LiYYYawen YangZLZhangpeng Li

Key Points

  • The aim is to develop a low-constraint wearable hydrogel sensor for better human-machine interaction.
  • Developed a novel low-constraint wearable hydrogel sensor patch (LCWHSP) utilizing a Fenton-like reaction.
  • Integrated light-curing 3D printing technology for optimizing printability and performance of hydrogel materials.
  • Utilized a signal acquisition system coupled with a deep learning model for hand movement recognition.
  • Achieved 98.60% accuracy in recognizing hand movements with the developed hydrogel sensor.
  • Cross-shaped structure reduced discomfort from high-density sensor arrays during use.
  • Demonstrated effective control of virtual interfaces and robotic arms in practical applications.

Abstract

ABSTRACT Embodied human‐machine interaction (EHMI) is predicated on the utilization of human motion signals as a medium of communication, with the objective of achieving a more natural, immersive, intuitive, and efficient interactive experience. The utilization of flexible hydrogel sensors as the EHMI core perception device remains limited due to discomfort caused by heavy loads and mechanical constraints. The present study developed a novel low‐constraint wearable hydrogel sensor patch (LCWHSP) based on the remote sensing mechanism of bionic spiders. The innovative integration of a Fenton‐like reaction into light‐curing 3D printing technology has facilitated the collaborative optimization of the printability and performance parameters of graphene‐Fe 3+ dynamically coordinated sodium alginate‐polyacrylamide (GFSP) double crosslinked hydrogel sensitive materials. The cross‐shaped structure design of the LCWHSP enables single‐point multidimensional sensing on the skin interface, thereby effectively reducing the sensation of wearing a foreign object caused by high‐density sensor arrays. High‐precision recognition of hand movements was achieved by utilizing a signal acquisition system and a deep learning analysis model (with an accuracy rate of 98.60%). Further using in typical EHMI scenarios, such as controlling virtual interfaces and manipulating robotic arms, fully validated its practical application potential.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a17dcbb3fad632b0f9d97a2https://doi.org/10.1002/adfm.76031
Ask AI
Helpful
Bookmark
Share
View Full Paper