An in-situ participatory approach for assistive robots: methodology and implementation in a healthcare setting

This paper presents a participatory design approach for developing assistive robots, addressing the critical gap between designing robotic applications and real-world user needs. Traditional design methodologies often fail to capture authentic requirements due to users' limited familiarity with robotic technologies and the disconnection between design activities and actual deployment contexts.

We propose a methodology centred on iterative in-situ co-design, where stakeholders collaborate with researchers using functional low-fidelity prototypes within the actual environment of use. Our approach comprises three phases: observation and inspiration, in-situ co-design through prototyping, which is the core of the methodology, and longitudinal evaluation. We implemented this methodology over 10 months at an intermediate healthcare centre. The process involved healthcare staff in defining functionality, designing interactions, and refining system behaviour through hands-on experience with teleoperated prototypes.

The resulting autonomous patrolling robot operated continuously across a two-month deployment. The evaluation through questionnaires on usability, usage and understanding of the robotic system, along with open-ended questions revealed diverse user adoption patterns, with five distinct personas emerging: enthusiastic high-adopter, disillusioned high-adopter, unconvinced mid-adopter, satisfied mid-adopter and non-adopter, which are discussed in detail.

During the final evaluation deployment, user feedback still identified both new needs and practical improvements, as co-design iterations have the potential to continue indefinitely. Moreover, despite some performance issues, the robot's presence seemed to generate a placebo effect on both staff and patients, while it appears that staff's behaviours were also influenced by the regular observation of the researchers. The obtained results prove valuable insights into long-term human-robot interaction dynamics, highlighting the importance of context-based requirements gathering.