Deep Learning in Diverse Intelligent Sensor Based Systems

Yanming Zhu, Min Wang, Xuefei Yin, Jue Zhang, Erik Meijering, Jiankun Hu

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)
28 Downloads (Pure)


Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

Original languageEnglish
Article number62
Pages (from-to)1-86
Number of pages86
Issue number1
Publication statusPublished - Dec 2022
Externally publishedYes


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