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Purpose GUARDIAN is a new technology-enabled ecosystem to support older adults and their carers in an indoor living environment. The GUARDIAN ecosystem is designed to address the different needs in different countries, creating an ecosystem accepted by the end user through a socially assistive robot (SAR) and two web applications (one for caregivers and one for frail older adults) that appropriately consider cultural differences. The project aim is to support older adults, formal carers and informal carers so that older adults can live safely at home for longer with enough comfort. Amongst others, the GUARDIAN Socially Assistive Robot (SAR) will be able to provide older adults with input and stimulation for activities (see also, Nap et al., 2018). This paper describes the iterative co-design and evaluation of GUARDIAN in three countries (Italy, Switzerland & The Netherlands) up to a first alpha working prototype of the SAR connected to the web interfaces that can be operated via a tablet. Method To gain insight into the perceptions, accessibility, acceptability and satisfaction with GUARDIAN, usability evaluations were performed using the IBM usability questionnaire (Lewis, 1995) and interviews were performed in the three different countries involving a total of 13 older adults, 14 informal carers and 15 formal carers. User requirements were translated into additional technical requirements for the further development of the SAR and supporting eco-system. Through a mixed-methods approach involving both project partners and end-users, it was explored how societal needs and concerns about the social and ethical impact (Lukkien et al., 2021) of using the SAR can be addressed in the design and use of GUARDIAN. Results and Discussion Based on the user-centered, value-sensitive and responsible innovation co-design results, three core functionalities have been identified for GUARDIAN: 1) monitoring the behavior, emotions, health and well-being of the older adult; 2) day structure support for e.g., medication and appointments; 3) social companionship. The results from the usability evaluations show that the proposed ecosystem is of high usability for both older adults and their carers, with positive mean scores above the scale mid- point of the usability questionnaire items. Only error handling and error messages scored slightly lower than the scale mid-point for the carers. From the interviews it appeared that GUARDIAN satisfies the user requirements, highlighting the feasibility of the application of the ecosystem as a supportive technology for monitoring, day structure and social companionship for older adults. Based on the interview results with the informal carers, it was found that they feel reassured through GUARDIAN about the well-being of the older adult. They reported positive attitude to the ability to monitor the relative at a distance; without being too intrusive to the older adult's privacy. Formal carers reported that GUARDIAN supports them to (re)activate their clients from a distance and GUARDIAN provides additional insights into the behavior and well-being of the client. From a responsible innovation perspective, some high-level opportunities were identified for shaping a more meaningful and ethically acceptable SAR, for instance in relation to personalization, safeguarding older adults' autonomy and promoting human connectedness in care practices supported by SARs. GUARDIAN has large potential to support independent living for older adults and (in)formal care at a distance. [ABSTRACT FROM AUTHOR]

Purpose The world is facing similar challenges in respect to the ageing population and shortages of care personnel, with dementia as one of the largest challenges (Gauthier et al., 2021). Across Europe and beyond, various dementia friendly eco-systems are in place with Active and Assisted Living (AAL) products and services to empower people with dementia (PwD) and their informal- and formal carers (Nap et al., 2018; Nap et al., 2020). However, it can be burdensome for (informal) carers to use multiple devices, applications and UI's, and particularly in care - where workload is high - bandwidth is limited to use multiple devices. With the HAAL project, The Netherlands, Italy, and Taiwan combine their expertise and experiences in development, co-design and evaluation to iteratively design an AI-driven cloud-based dashboard for formal carers to share data, collected from integrated sensors, that measure the well-being and quality of life of PwD. The dashboard is linked to a state-of-the-art bundle of HAAL products and services for PwD in various stages (see Reisberg et al., 1982) and their (informal)carers (see Figure 1.). Method All end-users are involved from the start of the HAAL project in co-design, business modelling, responsible innovation (Lukkien et al, 2021) and during the evaluation in the later phases of the project. Over 75 end-users will be involved in co-design, meaningful try-outs and formative evaluations. In the final summative evaluation, 90 end-users will be involved in 3 pilots (30 per country), consisting of triads of PwD, their informal and formal carers with a major focus on evaluating workload of carers and quality of life of PwD. Results and Discussion The first results from the co-design phases and meaningful try-outs show that formal carers are positive about a central AI-driven dashboard, as well as about a number of products and services in the HAAL bundle. In Taiwan, PwD were also involved in trying out the products and they showed their interest mainly in serious games and social robotics. In addition, within the HAAL project, a first UI design of the AI-driven dashboard has been iteratively developed, along with the software and the hardware architecture. Responsible innovation workshops supported in an early critical assessment of the merits and perils of AI in care for PwD. In HAAL, PwD, their relatives, and care professionals are the focal point for the testing, development, and design. In the following phases of the HAAL project, we will further co-design with these groups to assess the bundle of selected technologies, learn from the best practices and eventually develop and evaluate the AI-driven dashboard that offers the most useful solutions for dementia care. [ABSTRACT FROM AUTHOR]