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<p>Improper pen-holding posture can lead to increased pressure on the finger joints of school-aged children, muscle strain in the wrists and arms, illegible handwriting and slow writing speed. To address this challenge, we developed a PVA-CNF-MXene (PCM) hydrogel sensor via a repeated freeze-thaw method, which exhibits excellent mechanical properties (maximum strain of 450%), high sensitivity (GF = 3.479), fast response (~ 0.2&#xa0;s), and fatigue resistance (&gt; 4,000 cycles). Based on this material, we constructed an intelligent human-computer interaction system for self-adaptive writing posture monitoring. The sensor maintains good stability across various ranges, capable of monitoring both large body movements and subtle facial activities. More importantly, the integrated system achieves 95.83% accuracy in identifying incorrect writing postures and provides real-time corrective feedback, while also enabling handwriting recognition (94.5% accuracy) and personalized user identification (96.88% accuracy). This work demonstrates the great potential of MXene-based hydrogels in next-generation human-computer interaction applications.</p> Graphical abstract <p></p>

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Smart MXene-hydrogel hybrid platform for self-adaptive writing posture monitoring and autonomous learning closed-loop feedback correction

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Weijia Huo,
  • Ruitao Yu,
  • Long Zhao,
  • Ruibin Zhao,
  • Muze Li,
  • Shunguang Yu,
  • Yapeng Xu,
  • Tong Liu,
  • Minghui Cao

摘要

Improper pen-holding posture can lead to increased pressure on the finger joints of school-aged children, muscle strain in the wrists and arms, illegible handwriting and slow writing speed. To address this challenge, we developed a PVA-CNF-MXene (PCM) hydrogel sensor via a repeated freeze-thaw method, which exhibits excellent mechanical properties (maximum strain of 450%), high sensitivity (GF = 3.479), fast response (~ 0.2 s), and fatigue resistance (> 4,000 cycles). Based on this material, we constructed an intelligent human-computer interaction system for self-adaptive writing posture monitoring. The sensor maintains good stability across various ranges, capable of monitoring both large body movements and subtle facial activities. More importantly, the integrated system achieves 95.83% accuracy in identifying incorrect writing postures and provides real-time corrective feedback, while also enabling handwriting recognition (94.5% accuracy) and personalized user identification (96.88% accuracy). This work demonstrates the great potential of MXene-based hydrogels in next-generation human-computer interaction applications.

Graphical abstract