Cai Mingchen, Sun Hao, Yang Tianyue, Hu Hongxin, Li Xubing, Jia Yuan
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Micromachines (Basel). 2025 Jul 31;16(8):902. doi: 10.3390/mi16080902.
Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence.
持续监测环境和生理参数对于早期诊断、实时决策以及智能系统自适应至关重要。生物微机电系统（BioMEMS）传感器的最新进展显著增强了我们实时跟踪关键指标的能力。然而，持续监测需要可持续的能源供应解决方案，特别是在资源有限的地区进行现场能量补充。人工智能（AI），尤其是大语言模型，为解释这些传感器产生的大量数据提供了新途径。尽管有这种潜力，但将自供电的BioMEMS传感与基于AI的分析相结合的完全集成系统仍处于开发的早期阶段。本综述首先考察BioMEMS传感器的发展历程，重点关注传感材料、微/纳尺度架构以及制造技术方面的进展，这些进展使传感器具备高灵敏度、灵活性和生物相容性，以用于持续监测应用。然后，我们考察能量收集技术的最新进展，如压电纳米发电机、摩擦电纳米发电机和湿气发电机，这些技术使自供电的BioMEMS传感器能够持续运行并减少对传统电池的依赖。最后，我们讨论AI在BioMEMS传感中的作用，特别是在预测分析中，以分析持续监测数据、识别模式、趋势和异常，并将这些数据转化为可采取行动的见解。这一全面分析旨在为未来的持续BioMEMS传感提供路线图，揭示结合材料科学、能量收集和人工智能所释放的潜力。
