Evaluating Digital Twin Visualizations for Safety and Trust in Robot-Assisted Dressing

People with physical impairments often face difficulties in performing daily tasks such as dressing, leading to dependence on caregivers. While robotic manipulators can provide valuable assistance, close physical interaction raises concerns over safety, comfort, and trust, which can limit adoption. This paper introduces the Assistive Robot Twin (ART) framework, a real-time digital twin that models both the user and the robot to enhance transparency during robot-assisted dressing. ART integrates two visual safety features: Bounding Boxes (BBs), which define static or dynamic protective zones around critical regions, and Trajectory Visualisation (TV), which displays planned robot movements in real time. We conducted a within-subject study with 36 participants mimicking stroke-related mobility impairment, evaluating six BB/TV configurations using validated interaction quality and system usability questionnaires. The results show that BBs significantly improved perceived safety ( \(p<0.001\) ), reduced discomfort ( \(p<0.001\) ), and increased trust ( \(p<0.001\) ), with dynamic BBs providing the greatest safety benefits. TV significantly enhanced overall system usability ( \(p=0.007\) ), confidence, and predictability of robot actions. While the study focuses on perceived interaction quality in a controlled setting with healthy participants, the results provide foundational evidence for the design of transparent assistive systems prior to clinical deployment.