Pre-Deployment Perspectives on Humanoid Robots in Long- Term Care: Stakeholder Insights from a Real-world Lab

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

The integration of humanoid robots in long-term care settings is gaining attention due to demographic challenges and staff shortages. This study provides robotics engineers with concrete, early-stage design guidance for humanoid robots derived directly from real-world user needs in long-term care settings, enabling the development of more usable, acceptable, and effective products. Semi-structured interviews conducted before the deployment of a humanoid robot explored expectations, needs, and concerns regarding robotic assistance in daily routines. The study provides qualitative insights into the lives of both residents and staff, highlighting key areas where humanoid robotic support is anticipated. Findings suggest strong demand for assistance with everyday tasks, such as closing doors or windows, and openness to integration if user needs are met. Both groups recognize the potential to alleviate physical burdens and streamline daily tasks, yet concerns about usability, reliability, and ethical implications remain. This work demonstrates how qualitative insights can be systematically translated into actionable early-stage design considerations, bridging human-centered research and engineering practice. By grounding development in user expectations and real-world contexts, the approach supports the creation of functionally relevant and socially accepted technologies. The study highlights the value of real-world lab methodologies for aligning innovation with care practices and shows how non-technical user needs can be converted into early technical design directions. Addressing stakeholder concerns early is essential for successful integration into daily care routines.

Article activity feed