Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

TLDR

Sensors and algorithms are fundamental to intelligent systems, yet performance remains limited by rigid, bulky sensors that cannot conform to irregular surfaces for high‑quality data acquisition. The paper reviews how integrating stretchable electronics with machine learning advances bioelectrical signal recognition, tactile perception, and multimodal integration to accelerate perception and reasoning in human–machine interfaces, healthcare, and robotics. Skin‑like stretchable sensors, characterized by high conformability, low modulus, and light weight, are combined with machine‑learning algorithms to enable high‑quality data acquisition on irregular surfaces, and the review discusses current challenges and future directions.

Abstract

Abstract 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.