Commanding an assistive robotic arm in activities of daily living. Turning users into partners: Co-designing a robotic arm command system / Steuerung eines Assistenzroboterarms bei Aktivitäten des täglichen Lebens. Nutzer:innen zu Partner:innen machen: Gemeinsame Entwicklung eines Steuerungssystems für Roboterarme

Abstract Background Assistive robotic systems hold strong potential to improve autonomy and quality of life (QoL) for individuals with tetraplegia by supporting activities of daily living (ADLs). This project focused on developing a user-centered command structure and graphical user interface (GUI) for a robotic arm tailored to this population. Key goals included maximizing usability, managing robotic control states effectively, and enabling intuitive interaction and semi-automated task execution. The study was guided by three key research questions addressing how user needs can be effectively incorporated into control-structure design, which interface elements most enhance usability for individuals with tetraplegia, and how robotic control states can be managed to ensure safe and reliable everyday operation. Methods A mixed-methods approach was used, incorporating surveys, interviews, and observations to identify user needs. These insights informed iterative prototyping of a custom GUI and command system based on a Finite State Machine (FSM) architecture. The design emphasized adaptability, clarity, and control reliability, with user feedback guiding ongoing refinements. Results System evaluation through user testing and System Usability Scale assessments indicated high satisfaction. Users especially valued the intuitive GUI layout, task-specific control modes, and semi-automated functions, which improved task efficiency while maintaining user agency. Conclusion This study presents a validated framework for designing assistive robotic systems for individuals with severe motor impairments. The integration of user-centered design principles and FSM-based control supports both functionality and accessibility. Future work should explore real-world deployment, increased task automation, and integration with alternative input methods to enhance usability and clinical relevance.