Mild cognitive impairment (MCI) is an early stage of cognitive decline that significantly increases the risk of dementia, making early interventions crucial for maintaining cognitive health in older adults. Our research takes a co-design approach to understand, design, and evaluate how socially assistive robots and a virtual human can promote lifestyle changes for people with MCI (pwMCI), potentially improving their cognitive health. Through an iterative refinement process, we aim to develop adaptable, user-friendly, and sustainable technologies that foster long-term engagement across diverse settings. Ultimately, our goal is to improve cognitive health, quality of life, emotional well-being, and loneliness in pwMCI.

This study investigated whether attitudes and emotions towards robots predicted acceptance of a healthcare robot in a retirement village population. Residents (n = 32) and staff (n = 21) at a retirement village interacted with a robot for approximately 30 minutes. Prior to meeting the robot, participants had their heart rate and blood pressure measured. The robot greeted the participants, assisted them in taking their vital signs, performed a hydration reminder, told a joke, played a music video, and asked some questions about falls and medication management. Participants were given two questionnaires; one before and one after interacting with the robot. Measures included in both questionnaires were the Robot Attitude Scale (RAS) and the Positive and Negative Affect Schedule (PANAS). After using the robot, participants rated the overall quality of the robot interaction. Both residents and staff reported more favourable attitudes (p <; .05) and decreases in negative affect (p <; .05) towards the robot after meeting it, compared with before meeting it. Pre-interaction emotions and robot attitudes, combined with post-interaction changes in emotions and robot attitudes, were highly predictive of participants' robot evaluations (R = .88, p <; .05). The results suggest both pre-interaction emotions and attitudes towards robots, as well as experience with the robot, are important areas to monitor and address in influencing acceptance of healthcare robots in retirement village residents and staff. The results support an active cognition model that incorporates a feedback loop based on re-evaluation after experience.

As the proportion of people in the older age groups grows, demands on care providers increase. The ability of robotic technology to meet these demands is limited by a lack of acceptance by older people. This study investigates which tasks staff and residents in a retirement village would like a robot to assist with, as well as their attitudes towards robots and preferences for their appearance. Findings show that residents are more positive about robots than staff, and participants prefer a silver robot of 1.25 m height, with wheels and a screen on the body. Residents would most like the robot to assist with detecting falls, turning on and off appliances, lifting, cleaning, medication reminding, making phone calls and monitoring location. Making robots that fit these preferences may increase the acceptance of robotic assistants by older people.

Human-robot interaction (HRI) and user acceptance become critical when service robots start to provide a variety of assistance to users on a personal level. Limited research to date has studied the influence of users' attributes (such as age and gender) on the acceptance of service robots and the implications for HRI design. This paper describes the development of a social interactive healthcare robot named Charles, capable of measuring blood pressure. Using blood pressure monitoring as the service scenario, a user study was conducted to investigate the differences between two age groups (40 to 65 years and over 65 years) in attitudes and reactions before and after their interactions with Charles. The results showed few differences between the two age groups. A significant gender effect was found, with males having a more positive attitude toward robots in healthcare than females. This study reveals the importance of considering gender issues in the design of healthcare robots for older people. Overall, the performance of the robot was rated high, however the participants expressed desires to have more interactiveness and a better voice from the robot. According to our sample, age need not be a barrier to users' acceptance of healthcare robots.