Improving Interaction Comfort in Authoring Task in AR-HRI through Dynamic Dual-Layer Interaction Adjustment

Yunqiang Pei, Kaiyue Zhang, Hongrong yang, Yong Tao, Qihang Tang, Jialei Tang, Guoqing Wang, Zhitao Liu, Ning Xie, Peng Wang, Yang Yang, Heng Tao Shen

Abstract: Previous research has demonstrated the potential of Augmented Reality in enhancing psychological comfort in Human-Robot Interaction (AR-HRI) through shared robot intent, enhanced visual feedback, and increased expressiveness and creativity in interaction methods. However, the challenge of selecting interaction methods that enhance physical comfort in varying scenarios remains. This study purposes a dynamic dual-layer interaction adjustment mechanism to improve user comfort and interaction efficiency. The mechanism comprises two models: an general layer model, grounded in ergonomics principles, identifies appropriate areas for various interaction methods; a individual layer model predicts user discomfort levels using physiological signals. Interaction methods are dynamically adjusted based on continuous discomfort level changes, enabling the system to adapt to individual differences and dynamic changes, thereby reducing misjudgments and enhancing comfort management. The mechanism's success in authoring tasks validates its effectiveness, significantly advancing AR-HRI and fostering more comfortable and enhancing efficient human-centered interactions.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Experience] Interactions and Quality of Experience

Relevance To Conference: We improves the discomfort prediction from a discrete model to a continuous index model by fusing multimodal physiological signals in individual layer modeling, to better adapt to individual differences and dynamic changes, reduce misjudgment, and effectively assist participants in managing discomfort.

Submission Number: 3578