Recent progress on artificial intelligence-enhanced multimodal sensors integrated devices and systems

Abstract Multimodal sensor fusion can make full use of the advantages of various sensors, make up for the shortcomings of a single sensor, achieve information verification or information security through information redundancy, and improve the reliability and safety of the system. Artificial intelligence (AI), referring to the simulation of human intelligence in machines that are programmed to think and learn like humans, represents a pivotal frontier in modern scientific research. With the continuous development and promotion of AI technology in Sensor 4.0 age, multimodal sensor fusion is becoming more and more intelligent and automated, and is expected to go further in the future. With this context, this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems. Based on the concept and principle of sensor technologies and AI algorithms, the theoretical underpinnings, technological breakthroughs, and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics, healthcare, and environmental monitoring are highlighted. Through a comparative study of the dual/tri-modal sensors with and without using AI technologies (especially machine learning and deep learning), AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance, data processing, and decision-making capabilities. Furthermore, the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors, and offers a prospective outlook on the forthcoming advancements.