Soft Electronics for Health Monitoring Assisted by Machine Learning - Qiao, Yancong; Luo, Jinan; Cui, Tianrui; Liu, Haidong; Tang, Hao; Zeng, Yingfen; Liu, Chang; Li, Yuanfang; Jian, Jinming; Wu, Jingzhi; Tian, He; Yang, Yi; et al. (Nano-Micro Letters, 2023)
Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed. Highlights: 1 This review introduces soft electronics for health monitoring assisted by machine learning, and discusses soft materials, physiological signals, and machine learning algorithms in sequence and their relationships. 2 The principles of classic machine learning algorithms and neural network algorithms are summarized and explained by representative examples combining with soft electronics. 3 The potential challenges of soft electronics assisted by machine learning especially in health monitoring field are outlined, and future research directions are outlooked.