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<p>To break through the bottlenecks of traditional implantable medical devices, which rely on limited-life batteries and lack intelligent analysis capabilities. This research has developed an innovative solution that combines self-powered with intelligent monitoring. Distinct from existing biocompatible TENGs, this work features a unique design of the polyethylene oxide/BaTiO<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> (PBF) composite film. In this design, Polyethylene oxide served as the biocompatible matrix, BaTiO<sub>3</sub> acted as the triboelectric performance enhancer, and Fe<sub>3</sub>O<sub>4</sub> functioned as the magnetic functional component. This rational combination overcame the inherent defects of a single material and achieved a synergistic effect of biocompatibility, high triboelectric performance, and macroscopic magnetism. Based on the potential of triboelectric nanogenerators (TENGs) in self-powered sensing and the utilization of endogenous mechanical energy in the human body (e.g. heartbeat and breathing), this research synthesized a biocompatible PBF composite film. This film effectively enhances its triboelectric properties and endows the material with macroscopic magnetism by introducing functional components. After assembly with the silicone triboelectric layer, a biocompatible implantable TENG (BI-TENG) was constructed. The electrical output performance of this BI-TENG is outstanding, featuring an open-circuit voltage of 286.1&#xa0;V, a short-circuit current of 19.3 µA, and a peak output power density reaching 951.2 mW·m<sup>− 2</sup>. Furthermore, this research also constructed a BI-TENG-machine learning (ML) collaborative intelligent system. A key contribution of this work is the validation that the BI-TENG-ML system can not only utilize BI-TENG to monitor motion signals in real time but also conduct accurate intelligent analysis of these high-dimensional and nonlinear signals through ML technology, overcoming the limitation of traditional threshold-based signal interpretation. It aims to achieve more intelligent and proactive health monitoring and disease management, laying the foundation for the next generation of permanent self-powered intelligent implantable systems.</p> Graphical Abstract <p></p>

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Self-powered human motion health monitoring and machine learning analysis system based on magnetic implantable triboelectric nanogenerators

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Huijing Xiang,
  • Wei Zhang,
  • Chuanzi Deng,
  • Xing Ma,
  • Lin Peng,
  • Tong Wu,
  • Qingfei Shen,
  • Xia Cao

摘要

To break through the bottlenecks of traditional implantable medical devices, which rely on limited-life batteries and lack intelligent analysis capabilities. This research has developed an innovative solution that combines self-powered with intelligent monitoring. Distinct from existing biocompatible TENGs, this work features a unique design of the polyethylene oxide/BaTiO3/Fe3O4 (PBF) composite film. In this design, Polyethylene oxide served as the biocompatible matrix, BaTiO3 acted as the triboelectric performance enhancer, and Fe3O4 functioned as the magnetic functional component. This rational combination overcame the inherent defects of a single material and achieved a synergistic effect of biocompatibility, high triboelectric performance, and macroscopic magnetism. Based on the potential of triboelectric nanogenerators (TENGs) in self-powered sensing and the utilization of endogenous mechanical energy in the human body (e.g. heartbeat and breathing), this research synthesized a biocompatible PBF composite film. This film effectively enhances its triboelectric properties and endows the material with macroscopic magnetism by introducing functional components. After assembly with the silicone triboelectric layer, a biocompatible implantable TENG (BI-TENG) was constructed. The electrical output performance of this BI-TENG is outstanding, featuring an open-circuit voltage of 286.1 V, a short-circuit current of 19.3 µA, and a peak output power density reaching 951.2 mW·m− 2. Furthermore, this research also constructed a BI-TENG-machine learning (ML) collaborative intelligent system. A key contribution of this work is the validation that the BI-TENG-ML system can not only utilize BI-TENG to monitor motion signals in real time but also conduct accurate intelligent analysis of these high-dimensional and nonlinear signals through ML technology, overcoming the limitation of traditional threshold-based signal interpretation. It aims to achieve more intelligent and proactive health monitoring and disease management, laying the foundation for the next generation of permanent self-powered intelligent implantable systems.

Graphical Abstract