This study presents a computer vision-based optical strain (CVOS) sensor system that integrates computer vision with streamlined microfabrication techniques to overcome challenges associated with soft strain sensors, such as low reproducibility, susceptibility to environmental noise, and short lifetimes. The CVOS sensor uses an easily fabricated soft silicone substrate with micro-markers and a tiny camera for highly sensitive marker detection. Real-time multiaxial strain mapping enables measurement of complex multi-directional strain patterns, increasing scalability. Results indicate the CVOS sensor is a promising approach for developing highly sensitive and versatile human-machine interfaces.

Sunguk Hong, Vega Pradana Rachim, Jin-Hyeok Baek, Sung-Min Park