Flexible tactile sensors based on the electromagnetic induction (FTS-EMI) have emerged as a prominent research focus, owing to their exceptional self-powered potential, high robustness, and remarkable tolerance to complex deformations. This review elaborates on the fundamental working mechanisms and analyzes the spatial magnetic field distribution of magnets, coil performance and the electromechanical coupling characteristics of sensor systems, providing theoretical support for achieving mechano-magneto-electrical coupling synergy. By constructing a three-layer analytical framework of “fabrication process–structural design–backend processing”, the key performance parameters of three typical sensor structures are compared, including sensitivity, detection range and response time. A targeted strategy is also provided to enhance the anti-interference characteristics and detection accuracy of the FTS-EMI. Some diversified application scenarios were summarized, emphasizing the significant advantages of FTS-EMI in the fields of wearable electronic devices and intelligent robots. Despite performance improvements, challenges such as the difficulty in detecting static pressure and limited self-powered energy still persist. The future of FTS-EMI hinges on the integration of multimodal sensing principles, optimization of programmable magnetic materials, research on advanced signal processing schemes and interdisciplinary collaboration aimed at exploring more potential applications of flexible tactile sensing. • To achieve the mechano-magneto-electric coupling synergy of FTS-EMI, a detailed analysis is conducted respectively from three aspects of the magnet, coil and sensor system by combining the electromagnetic induction theory and dynamic models. • The preparation processes of coils and flexible magnets were classified and introduced in detail respectively. • The performance advantages of sensors with magnetic microcolumn structures, biomimetic structures and film structures are elaborated. • Based on different application backgrounds, signal processing methods and application are categorized and introduced.
Zhang et al. (Fri,) studied this question.
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