Toward Optimized AR-Based Human-Robot Interaction Ergonomics: Modeling and Predicting Interaction Comfort

Yunqiang Pei, Bowen Jiang, Kaiyue Zhang, Ziyang Lu, Mingfeng Zha, Guoqing Wang, Zhitao Liu, Ning Xie, Yang Yang, Hengtao Shen

Abstract: Augmented Reality in Human-Robot Interaction (AR-HRI) boosts user experience. The key challenge is refining interaction methods to minimize discomfort and enhance quality. This AR - HRI study uses Galvanic Skin Respons (GSR) to predict and improve user comfort. User studies tested interaction strategies in an AR environment. A machine learning model, developed from GSR data, predicted comfort levels and informed strategy changes. Comfort metrics were visualized every second using Hololens 2, creating an AR - HRI comfort system. The method improved user comfort, provided a new AR-HRI metric, and highlighted future research opportunities.