Redefining Vibration Sensing: AI-Driven Analytics, Self-Powered Systems, and Multimodal Fusion: A Review

The evolution of vibration sensing technologies has driven critical advancements in structural safety monitoring (SSM), environmental vibration monitoring (EVM), human–machine interaction (HMI), and biomedical health monitoring (BHM). However, despite decades of progress, fundamental limitations persist: low-frequency signal attenuation, environmental instability, and the lack of self-powered solutions continue to constrain real-world applicability. Existing sensor technologies face inherent tradeoffs—piezoelectric sensors suffer from brittleness, piezoresistive sensors exhibit temperature drift, capacitive sensors struggle with long-term stability, triboelectric sensors remain limited by material degradation, and fiber-optic sensors face high costs and integration challenges. These challenges hinder the development of next-generation intelligent monitoring systems. This review presents a systematic, quantitative benchmarking of major vibration sensor technologies, comparing their performance in sensitivity, frequency response, environmental resilience, and scalability. Unlike conventional surveys, we bridge theoretical foundations with practical deployment challenges, revealing key trends in AI-assisted analytics, multimodal sensor fusion, and energy-autonomous architectures. Furthermore, we propose a roadmap for overcoming existing technological barriers, emphasizing bio-inspired materials, hybrid energy harvesting, and physics-informed machine learning for adaptive sensing. By redefining the intersection of sensor physics and intelligent systems, this work establishes a framework for the next generation of high-performance vibration sensors with enhanced robustness, precision, and sustainability.