3^rd International Conference on Computer Science and Software Engineering (CSSE 2025)

Accepted Papers

Ai-driven Peripheral Device Management and Assistive Multi-modal Input for Wireless Human-computer Interaction

Minzhou Wang¹ and Peijin Du², ¹Independent Researcher, Charlotte, USA, ²Independent Researcher, Los Angeles, USA

ABSTRACT

This paper explores AI-assisted peripheral management techniques that improve traditional input methods through modular and linear layouts combined with voice-based support. Modular and linear approaches let users construct macros with temporal logic, enabling faster setup and more intuitive execution. Voice models extend accessibility by allowing disabled users to configure and later trigger complex key combinations through simplified inputs. Two experiments evaluated these methods: one compared modular/linear layouts with free-design in terms of setup time, consistency, accuracy, and satisfaction; the other tested AI-optimized layouts with gaze heatmaps. Results show faster setup, fewer errors, and improved accessibility.

Keywords

Human-Computer Interaction, Machine Learning, Voice-Based AI, Accessibility, Game and Software Engineering, Natural Language Processing, Software Engineering, Automation.