Skin‐Conformal Motion Monitoring Film for Deep‐Learning‐Based Immersive Extended Reality

Abstract Immersive technologies like virtual reality (VR) and augmented reality (AR) are rapidly advancing, but current motion tracking systems face significant limitations. Camera‐based methods struggle with occlusion and environmental sensitivity, while existing skin‐applied sensors suffer from poor adhesion or lack of reusability. This research introduces a deep‐learning‐based motion monitoring system utilizing a skin‐conformal stretchable film for reliable and immersive motion tracking in VR and AR applications. The approach enhances skin adhesion through elastomer cross‐linking weakening and microwrinkle formation. The developed skin‐conformal motion monitoring film (SCMF) demonstrates 5.3 times higher skin adhesion (0.0218 N mm −1 ) compared to conventional elastomer. With its improved adhesion, the SCMF demonstrates high conformality to skin movements, accurately following dynamic motions of the skin, resulting in reliable strain and bending angle signals. By applying the SCMF to finger joints, a system capable of reliably monitoring finger movements in a VR environment and classifying baseball grips using convolutional neural network (CNN) algorithms can be developed. The application is extended to 7 upper body parts, developing a real‐time workout monitoring system in AR using change point detection and 2D CNN algorithms. The approach enhances accuracy and adaptability of motion tracking across immersive technologies, potentially transforming user experiences in various sectors.