MIUIC: A Human-Computer Collaborative Multimodal Intention-Understanding Algorithm Incorporating Comfort Analysis.

Abstract The naturalness and safety of human-computer interaction have always been primary research focuses in the field of human-computer interaction. This paper proposes a multimodal intention understanding algorithm (MIUIC), which incorporates comfort analysis, as a solution to address the issues of low intention understanding rate, weak interaction, and weak collaboration that are often observed in most massage systems. The algorithm efficiently fuses multimodal data based on objective implicit information to address the challenge of low intention understanding rates caused by non-standard expression of natural behavior. Moreover, this algorithm incorporates comfort reasoning to detect and address intentions related to security threats while providing the ability for robots to make behavioral decisions through inverse active interaction, leading to more equitable human-robot interactions. To test the validity and safety of the MIUIC algorithm, we embedded the algorithm into a mechanical arm massage system. Subsequently, 45 elderly volunteers were invited to participate in experimental tests. Finally, to verify the validity and safety of the MIUIC algorithm, we assessed the algorithm in terms of four aspects, including multimodal intention recognition rate, the ability to reduce data dispersion, the intention enhancement rate under reverse human-machine interaction, and the rate of avoiding dangerous intentions. In conclusion, the MIUIC algorithm enhances the intention understanding rate and promotes. [ABSTRACT FROM AUTHOR]