Machine Learning‐Enabled Smart Sensor Systems

• Main Authors: Nam Ha, Kai Xu, Guanghui Ren, Arnan Mitchell, Jian Zhen Ou
• Format: Article
• Language: English
• Published: Wiley 2020-09-01
• Series: Advanced Intelligent Systems
• Subjects: deep neural networks; machine learning; smart sensor applications; smart sensors; smart systems
• Online Access: https://doi.org/10.1002/aisy.202000063

Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.