The paper "AI-Driven Sensing Technology: Review" by Long Chen et al. explores the integration of machine learning (ML) and deep learning (DL) algorithms with sensor technologies, highlighting their significant impact on various fields such as industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The authors discuss how these advanced technologies enhance sensor performance, accuracy, and adaptability, and address the challenges and future trends in this area. Key areas of focus include: 1. **Sensor Design Assisted by ML/DL**: ML/DL algorithms are used to optimize sensor design, improve signal processing, and enhance performance. Techniques like inverse design and performance enhancement are discussed, with examples of capacitive pressure sensors and fiber Bragg grating sensors. 2. **Calibration and Compensation**: ML/DL algorithms are employed to reduce signal drift caused by environmental factors and to automatically compensate for errors during sensor use. Examples include temperature compensation for pressure sensors and noise reduction in acoustic signal processing. 3. **Recognition and Classification**: AI algorithms are used for object identification, behavior recognition, and health monitoring. Applications range from robotic perception and gesture recognition to human posture and health status detection. The paper provides detailed examples of these applications, including tactile sensors for robotic fingertips and AI-driven sleep monitoring systems. The authors conclude by discussing the challenges and future directions in AI-driven sensing technology, emphasizing the need for more universal and robust models that can handle complex multi-field responses and long-term performance changes.The paper "AI-Driven Sensing Technology: Review" by Long Chen et al. explores the integration of machine learning (ML) and deep learning (DL) algorithms with sensor technologies, highlighting their significant impact on various fields such as industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The authors discuss how these advanced technologies enhance sensor performance, accuracy, and adaptability, and address the challenges and future trends in this area. Key areas of focus include: 1. **Sensor Design Assisted by ML/DL**: ML/DL algorithms are used to optimize sensor design, improve signal processing, and enhance performance. Techniques like inverse design and performance enhancement are discussed, with examples of capacitive pressure sensors and fiber Bragg grating sensors. 2. **Calibration and Compensation**: ML/DL algorithms are employed to reduce signal drift caused by environmental factors and to automatically compensate for errors during sensor use. Examples include temperature compensation for pressure sensors and noise reduction in acoustic signal processing. 3. **Recognition and Classification**: AI algorithms are used for object identification, behavior recognition, and health monitoring. Applications range from robotic perception and gesture recognition to human posture and health status detection. The paper provides detailed examples of these applications, including tactile sensors for robotic fingertips and AI-driven sleep monitoring systems. The authors conclude by discussing the challenges and future directions in AI-driven sensing technology, emphasizing the need for more universal and robust models that can handle complex multi-field responses and long-term performance changes.