| Popis: |
Assistive robots are intended to solve problems associated with the aging population. However, previous Human-Robot Interaction (HRI) research has predominantly focused on top-down approaches, not so much taking into account complexities of stakeholders that are involved in designing, developing, and using robots in diverse living spaces. The aim of this PhD thesis is to explore bottom-up approaches for supporting older adults with robots. This involves i) understanding longitudinal experiences with AAL systems in the field, and ii) engaging older adults in the design of robots. To get an understanding of the context in which robots are envisioned to be used, two longitudinal studies on older adults' experiences with assistive technology were conducted in diverse living spaces. To engage older adults in the design of robots, challenges for participatory design (PD) for robots were identified in a study, and subsequently addressed with a methodological tool to co- imagine robots in living spaces. This thesis offers three main contributions. The first contribution is an understanding of longitudinal experiences with assistive technology in diverse living spaces. In order to design robots for older adults, it is important to understand and promote self-determination needs in socio-technical networks. Also, the way technology is put to work in practice needs to be understood and designed for. The second contribution provides an understanding of methodological challenges when engaging older adults in the design of robots. Key PD challenges for robots include knowledge transfer in multidisciplinary HRI teams, grounding, and terminology. To further explore and address PD challenges, a card- based tool is presented. The use of this tool also shows that spacial requirements need to be specified for robots in order to be perceived trustworthy. The third contribution provides design considerations for supporting older adults with robots. These include the need to design for relatedness in communities (e.g., in families, and for collaborative use), personalization (e.g., social environments or privacy), learning (e.g., learning environments, or mutual learning), values (e.g., autonomy and privacy), and specific places and work practices (e.g., spatial configurations, and desired work roles). |
