Premium
Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces
Author(s) -
Wang Ming,
Wang Ting,
Luo Yifei,
He Ke,
Pan Liang,
Li Zheng,
Cui Zequn,
Liu Zhihua,
Tu Jiaqi,
Chen Xiaodong
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202008807
Subject(s) - computer science , robotics , artificial intelligence , sensor fusion , electronics , construct (python library) , human–machine system , machine learning , robot , human–computer interaction , nanotechnology , materials science , engineering , electrical engineering , programming language
Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high‐quality data acquisition. Skin‐like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed. These efforts aim to accelerate various perception and reasoning tasks for advanced intelligent applications, such as human–machine interfaces, healthcare, and robotics.