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<p>The inherent advantages of polymer materials, such as excellent flexibility, processability, and molecular structure tunability, make them critical materials for fabrication of multimodal flexible sensors, which can be applicated in complex scenarios to perceive and monitor diverse stimuli, particularly in physiological signal acquisition and human-machine interaction. However, the widespread existence of signal crosstalk and coupling phenomena, which originate from the intrinsic material properties and device integration strategies, severely compromise the performance and reliability of multimodal flexible sensors. This paper systematically reviews the strategies of signal processing to identify the correct signal for multimodal flexible sensors from multiple perspectives, containing signal processing based on material combination, structural optimization, differences of signal response characteristics, and artificial intelligence technology. Previously, the basic structural composition, commonly used materials, fundamental sensing mechanisms and common fabrication methods of flexible sensors are systematically introduced. Signal decoupling by extracting the characteristic differences of output signals in terms of sensing behavior dependent on test condition, time response and variations in amplitude, and utilizing differential measurement techniques are systematically discussed. Moreover, the fundamental types and principles of machine learning algorithms, as well as the advantages and disadvantages of them are also introduced in detail before discussing their application in signal decoupling and recognition. Simultaneously, the advantages of signal processing strategies based on machine learning compared with traditional signal processing strategies and their current limitations are elaborated in detail. Finally, the paper summarizes the persistent challenges in multimodal flexible sensing and offers a forward-looking perspective on the developmental trajectory of polymeric multimodal sensors, with the goal of providing a reference for future research and promoting the practical application of multimodal flexible sensors.</p>

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Research advances towards multidimensional signal processing strategies for polymeric multimodal flexible sensors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Jun Tong,
  • Bin Lan,
  • Min Wu,
  • Zhifeng Wang,
  • Haichen Zhang,
  • Wei Li,
  • Ruiqi Yuan,
  • Haichu Chen,
  • Lan Liao

摘要

The inherent advantages of polymer materials, such as excellent flexibility, processability, and molecular structure tunability, make them critical materials for fabrication of multimodal flexible sensors, which can be applicated in complex scenarios to perceive and monitor diverse stimuli, particularly in physiological signal acquisition and human-machine interaction. However, the widespread existence of signal crosstalk and coupling phenomena, which originate from the intrinsic material properties and device integration strategies, severely compromise the performance and reliability of multimodal flexible sensors. This paper systematically reviews the strategies of signal processing to identify the correct signal for multimodal flexible sensors from multiple perspectives, containing signal processing based on material combination, structural optimization, differences of signal response characteristics, and artificial intelligence technology. Previously, the basic structural composition, commonly used materials, fundamental sensing mechanisms and common fabrication methods of flexible sensors are systematically introduced. Signal decoupling by extracting the characteristic differences of output signals in terms of sensing behavior dependent on test condition, time response and variations in amplitude, and utilizing differential measurement techniques are systematically discussed. Moreover, the fundamental types and principles of machine learning algorithms, as well as the advantages and disadvantages of them are also introduced in detail before discussing their application in signal decoupling and recognition. Simultaneously, the advantages of signal processing strategies based on machine learning compared with traditional signal processing strategies and their current limitations are elaborated in detail. Finally, the paper summarizes the persistent challenges in multimodal flexible sensing and offers a forward-looking perspective on the developmental trajectory of polymeric multimodal sensors, with the goal of providing a reference for future research and promoting the practical application of multimodal flexible sensors.