The First Accessible Polygenic-weLLbeing (PLL) Robot for Culinary Skills Education and Healthcare Support for Vulnerable Groups in Wales

This research proposes and designs the Polygenic-weLLbeing (PLL) robot, an embodied service robot platform for domestic settings involving vulnerable groups. The system is designed to explore the integration of cooking skills education and dietary guidance, health and care support, and home Internet-of-Things control within a unified framework, with the aim of supporting user profiling and potential cross-module task coordination. To conduct a preliminary, prototype-level validation, this research adopted a mixed-methods approach and carried out two exploratory trials. The first was qualitative descriptive research in an aged-care facility setting, and the second was an embedded questionnaire survey conducted during cooking activities in an ethnic minority community. The results suggest that, in the older adult care setting, the PLL prototype showed initial indications of acceptability in terms of reminders, companionship, step-by-step guidance, and structured support, with older users showing a preference for concise language, clear steps, and a moderate pace of interaction. In the ethnic minority community setting, matching cultural dietary preferences, clear task guidance, and easy-to-understand voice prompts were associated with improved perceived acceptability and usefulness. The findings suggest that the design of healthcare robots for vulnerable groups should go beyond simply adding functions, but must also give due consideration to interactional comprehensibility, cultural adaptability, accessibility, and emotional support. As prototype research, this paper conducted scenario-based preliminary validation of two key modules of PLL, but it has not yet systematically evaluated multi-module coordination or the operation of the system. Future work is still needed to further verify its multi-module collaborative capability and its potential for sustained application in longer-term, larger-scale real-world settings.