Real-time multiaxial strain mapping using computer vision integrated optical sensors

Soft strain sensors pose great potential for emerging human-machine interfaces. However, their real-world applications have been limited due to challenges such as low reproducibility, susceptibility to environmental noise, and short lifetimes, which are attributed to nanotechnologies, including microfabrication techniques. In this study, we present a computer vision-based optical strain (CVOS) sensor system that integrates computer vision with streamlined microfabrication techniques to overcome these challenges and facilitate real-time multiaxial strain mapping. The proposed CVOS sensor consists of an easily fabricated soft silicone substrate with micro-markers and a tiny camera for highly sensitive marker detection. Real-time multiaxial strain mapping allows for measuring and distinguishing complex multi-directional strain patterns, providing the proposed CVOS sensor with higher scalability. Our results indicate that the proposed CVOS sensor is a promising approach for the development of highly sensitive and versatile human-machine interfaces that can operate long-term under real-world conditions.