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<p>Microneedle biosensors enable dynamic monitoring of interstitial fluid biomarkers, but remain constrained by sensing interface susceptibility to motion artifacts and the prohibitive energy consumption of wireless cloud-based processing. Here, we report a bio-inspired, self-anchoring microinterventional in-sensor computing system. By leveraging a starfish-inspired suction cup-microneedle self-anchoring mechanism, the system effectively counteracts microneedle extrusion, attenuating signal fluctuations by 38-fold and enhancing signal intensity by up to 5.49-fold compared to conventional planar devices. Crucially, the high-fidelity data acquisition reduces the computational burden, enabling the deployment of a lightweight algorithm (43 KB) on a coin-sized embedded circuit, achieving 98.68% diagnostic accuracy and a 120-h battery life via local closed-loop feedback. Validation in a porcine model confirmed the system’s capability to capture continuous biochemical dynamics. This co-design of a robust biomimetic interface and lightweight deep learning paves the way for next-generation wearables capable of performing high-fidelity, on-chip metabolic risk stratification in dynamic daily settings.</p>

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Microinterventional in-sensor computing system for real-time metabolic health assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

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  • Peidi Fan,
  • Haitao Zhang,
  • Xiaoyu Su,
  • Xuewei Yang,
  • Ying Liu,
  • Shuangfeng Pan,
  • Xunjia Li,
  • Yibin Ying,
  • Yuxiang Pan,
  • Jianfeng Ping

摘要

Microneedle biosensors enable dynamic monitoring of interstitial fluid biomarkers, but remain constrained by sensing interface susceptibility to motion artifacts and the prohibitive energy consumption of wireless cloud-based processing. Here, we report a bio-inspired, self-anchoring microinterventional in-sensor computing system. By leveraging a starfish-inspired suction cup-microneedle self-anchoring mechanism, the system effectively counteracts microneedle extrusion, attenuating signal fluctuations by 38-fold and enhancing signal intensity by up to 5.49-fold compared to conventional planar devices. Crucially, the high-fidelity data acquisition reduces the computational burden, enabling the deployment of a lightweight algorithm (43 KB) on a coin-sized embedded circuit, achieving 98.68% diagnostic accuracy and a 120-h battery life via local closed-loop feedback. Validation in a porcine model confirmed the system’s capability to capture continuous biochemical dynamics. This co-design of a robust biomimetic interface and lightweight deep learning paves the way for next-generation wearables capable of performing high-fidelity, on-chip metabolic risk stratification in dynamic daily settings.