Virtual reality (VR) can support healthy ageing, but few devices have been trialed with frail older adults to increase physical activity. We conducted a preliminary mixed-methods implementation evaluation of an omnidirectional VR treadmill and a static VR experience with seven older adults over a six-week period in a supported living facility. Frequency of use and pre-post physical functioning measures were collected, mainly to establish technology suitability based on person characteristics. Diary entries following technology use, resident focus group and staff interview revealed technology acceptance and perceived potential for increasing physical activity, health and wellbeing through accessing virtual environments, which motivated continued activity. Results demonstrated technology suitability for a range of older adults with various mobility and physical impairments. However, residents noted interest in a seated treadmill for physical activity without perceived risks of falls with standing treadmills. Staff raised considerations around care home implementations including usability, cost and space.

Codesign is important, but during pandemic lock-down needed to be done remotely. We scoped a digital audio-reminiscence tool for people with dementia in care homes with key stakeholders. Zoom focus groups, following prerecorded presentation, were conducted with 15 professionals, students, community participants, and three care home residents and staff member. The residents had problems hearing well. Seven residents in another home contributed by picture questionnaire. A follow-up interview was held with the technology developer. Participants praised the digital audio-reminiscence concept, noting potential for enhanced relationships, calming effects, and re-living memories. Suggested content included historical events or bespoke childhood memories. Concerns raised included triggering negative memories or complicated interfaces. The developer reported codesign increased product confidence. Remote codesign can be acceptable, at least among the more digitally able, providing a face-to-face alternative when needed but more preparation is required so disadvantaged groups like care home residents can be included in future.

Anthropomorphic robots may reduce loneliness in older people, however, acceptance is requisite for adoption. We collected the experiences of 10 people aged 80–92 who used a pre-market social robot, GenieConnect, for between 2 to 35 days during the COVID19 pandemic restrictions. GenieConnect is a table-top robot with a large face and animated eyes, designed for support and companionship. The robot asked ‘how are you feeling, Name’ each day and delivered lifestyle prompts such as medication reminders. We observed conflicting responses from participants - five expressed positive responses, three negative (two of these withdrew) and two neutral. Positive comments included ‘feeling not alone’; ‘having someone to talk to’; and enjoying being asked ‘how are you feeling’. Negative comments were mainly related to not liking the eyes. Design adaptations were made to increase acceptance. We conclude that robots like GenieConnect could reduce loneliness when a user-centred design approach is taken.

This paper reflects on four studies completed over the last 24 months, with social robots including Pepper, Paro, Joy for All cats and dogs, Miro, Pleo, Padbot and cheaper toys, including i) focus groups and interviews on suitable robot pet design, ii) surveys on ethical perceptions of robot pets, and iii) recorded interactions between stakeholders and a range of social robots. In total, up to 371 participants’ views were included across the analysed studies. Data was reviewed and mined for relevance to the use and impact of morphology types for social robots in health and social care. Results suggested biomorphic design was preferable over mechanomorphic, and speech and life-simulation features (such as breathing) were well received. Anthropomorphism demonstrated some limitations in evoking fear and task-expectations that were absent for zoomorphic designs. The combination of familiar, zoomorphic appearance with animacy, life-simulation and speech capabilities thus appeared to be an area of research for future robots developed for health and social care.

Socially assistive robots have the potential to help keep people with dementia cognitively active and entertained. This is important for their wellbeing. We explored staff perceptions of the acceptability of a new humanoid robot, Stevie, in an adult day care center for people with dementia. Stevie was deployed over 2 weeks in the day center to entertain 40 guests with dementia with three activities: (i) musical bingo, (ii) quizzes, and (iii) meet and greet. Nine staff members were asked what went well, whether the robot operated as expected, experiences of any technical issues, and the extent to which it hindered or aided their duties. Staff also rated guest engagement, guest enjoyment, and whether they were able to spend more time with guests. The sessions were successfully delivered. Staff reported that Stevie operated as expected, guest engagement with the robot was high, interactions with the robot were natural and the robot’s novelty helped engagement. Use of the robot gave staff more time to attend to guests’ needs. Suggested improvements included improvements to the robot’s voice and adding a pause function during activities. With greater autonomy and/or extended use of its telepresence functionality, use of Stevie could become cost effective in such settings.

With the global population aging at an alarming rate, the need to find alternative ways to deliver quality assistance is becoming a pressing concern for health and care systems. To promptly provide companion-like assistance, robots need to gain social intelligence in an autonomous way, without relying on human operators. The work described in this paper aims to develop a deep learning agent that, by means of convolutional neural network architecture in the decision making loop, could understand when and how, to interact with one, or more people, gathered in a room. This was done by training a robot to assess the level of user engagement at the initiation of the interaction, so that the robot could detect the person most willing to start interacting. The robot's performance as a deep learning agent was tested through an experiment with potential ''end-users'', following an iterative process, over four days. The deep learning agent was able to take the right decision 59% of the times by the end of the experiment, from an initial success rate of 44% on the first day, proving the potential of such technologies in this application field.

The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements.

The ageing of the population deeply impacts on the social costs relative to health care. The use of modern technologies is one of the most promising approaches, under current study, to reduce such impact. In this demonstration, we propose a framework that can be employed for at-home assessment of Mild Cognitive Impairment (MCI). It is composed by a set of digitalized cognitive tests, developed from their paper-and-pencil counterparts, and by a Virtual Caregiver, which oversees the test execution and provides instructions.

The world population is ageing at a dramatic rate, raising new challenges for social and health care systems. Sometimes, assistance can simply derive from a social interaction between a robotic platform and human users. In these cases, robots cannot rely on human operators. Therefore, they need to gain social intelligence in a fully autonomous way. The focus of this paper is on the initial steps needed to implement a completely autonomous robotic agent able to adapt itself to its users. For this reason, an interactive data collection was carried out to gather a dataset from which the robot could learn how to respond to its users in different situations. From these data, a first evaluation of the performances of the deep learning agent, embodied in the robot, has been completed. The agent was able to generalize to new sets of test data. The study explored how, using modern machine learning algorithms, a robot could learn to understand if, and how, to interact with one, or more people, gathered in a room. This was done by training a robot to read the level of the engagement of the users at the initiation of the interaction.

Good communication is vital in health care, both among health care professionals, and between health care professionals and their patients. And well-written documents, describing and/or explaining the information in structured databases may be easier to comprehend, more edifying, and even more convincing than the structured data, even when presented in tabular or graphic form. Documents may be automatically generated from structured data, using techniques from the field of natural language generation. These techniques are concerned with how the content, organization and language used in a document can be dynamically selected, depending on the audience and context. They have been used to generate health education materials, explanations and critiques in decision support systems, and medical reports and progress notes.

The use of a touch-screen public access health information system was evaluated by monitoring system usage, by interviews with an opportunistic sample of 90 users and by other surveys. To get the largest number of users, such a system needs to be sited in a highly visible setting where there are lots of people passing. For most people, privacy does not appear to be a problem. However, some groups may require more privacy and when siting in 'quieter' places, such as a library this may be more important than in busier anonymous places, or in places where health is a 'natural' topic of interest. Waiting rooms may not be the best sites.