This paper considers the dynamics of the emerging care robotics innovation ecosystem in the Finnish welfare services. Innovation ecosystems have both evolutionary nature as well as aspects of purposeful design, and we study the relevant actors, their roles, the accelerators and the barriers, by conducting a survey among relevant stakeholders in the innovation ecosystem. An online survey was conducted with a range of Finnish stakeholders (N=250): service actors (n=148) and research and development actors (n=102). The responses of the two groups were analysed with a pairwise t-test. The results show that a variety of stakeholders are needed in ecosystem. The role of micro-level actors, such as workmates and professional and private robot users, were considered as important. Service actors should also play a stronger role in the ecosystem. In particular, research and development actors seemed to be open to new stakeholders entering the ecosystem and highlighted the importance of collaboration between actors. The culture of piloting in Finland is accelerating the ecosystem, but attitudinal factors, such as fears and resistance to change are hindering its development. The ecosystem is dynamic, and the dynamics in the ecosystem seem to be largely based on social and cultural issues.

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A light-weight, wearable, wireless gaze tracker with integrated selection command source for human-computer interaction is introduced. The prototype system combines head-mounted, video-based gaze tracking with capacitive facial movement detection that enable multimodal interaction by gaze pointing and making selections with facial gestures. The system is targeted mainly to disabled people with limited mobility over their hands. The hardware was made wireless to remove the need to take off the device when moving away from the computer, and to allow future use in more mobile contexts. The algorithms responsible for determining the eye and head orientations to map gaze direction to on-screen coordinates are presented together with the one to detect movements from the measured capacitance signal. Point-and-click experiments were conducted to assess the performance of the multimodal system. The results show decent performance in laboratory and office conditions. The overall point-and-click accuracy in the multimodal experiments is comparable to the errors in previous research on head-mounted, single modality gaze tracking that does not compensate for changes in head orientation.

Exploring the specific field of care robot orientation generates many questions regarding the meaning, content and how it should be conducted. The issue is important due to the general digitalisation and implementation of welfare technology and care robots. The aim of the study was to explore perceptions of care robot orientation from the potential users' perspective. Data were collected by focus group interviews in Finland, Germany and Sweden. In all three countries, potential user groups were represented: older adults, relatives, professional caregivers and care service managers. A qualitative descriptive method was used for analysing data. The data revealed three aspects of care robot orientation: (1) What care robot orientation is, (2) Who needs it and by Whom it should be given and (3) How it should be performed. The need for care robot orientation is general in society. In the absence of knowledge about care robots, it is nearly impossible to know what to ask for or actually seek information about. Therefore, care robot orientation must be founded on agile implementation planning for care robots, with a firm basis in trustworthy knowledge and information and respecting individuals' wishes. This also gives rise to an ethical challenge when care robots are offered to people having reduced decision-making ability (dementia, cognitive impairment), along with the issue of who then should make the decision. The mapping of the What, Who/Whom and How aspects of care robot orientation offers a foundation for the creation of orientation models, which might facilitate structured and goal-oriented care robot orientation strategies.

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