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Abstract：
Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.
摘要：
机器学习和深度学习技术正在迅速推进传感技术的能力，带来准确性的显著提高，灵敏度,和适应性。这些进步在广泛的领域产生了显著的影响，包括工业自动化，机器人,生物医学工程，和民用基础设施监测。这种转型转变的核心在于人工智能（AI）与传感器技术的集成，专注于开发有效的算法，以驱动设备性能增强和各种生物医学和工程领域的新应用。这篇综述深入研究了ML/DL算法与传感器技术的融合，揭示了它们对传感器设计的深远影响，校准和补偿，物体识别,和行为预测。通过一系列示例性应用，该评论展示了AI算法显着升级传感器功能并扩大其应用范围的潜力。此外，它解决了利用这些技术进行传感应用时遇到的挑战，并提供了对未来趋势和潜在进步的见解。
