For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation "in the wild." The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.
BackgroundThis study is one of the first randomized controlled trials investigating cognitive behavioral therapy for insomnia (CBT-I) delivered by a fully automated mobile phone app. Such an app can potentially increase the accessibility of insomnia treatment for the 10% of people who have insomnia.ObjectiveThe objective of our study was to investigate the efficacy of CBT-I delivered via the Sleepcare mobile phone app, compared with a waitlist control group, in a randomized controlled trial.MethodsWe recruited participants in the Netherlands with relatively mild insomnia disorder. After answering an online pretest questionnaire, they were randomly assigned to the app (n=74) or the waitlist condition (n=77). The app packaged a sleep diary, a relaxation exercise, sleep restriction exercise, and sleep hygiene and education. The app was fully automated and adjusted itself to a participant’s progress. Program duration was 6 to 7 weeks, after which participants received posttest measurements and a 3-month follow-up. The participants in the waitlist condition received the app after they completed the posttest questionnaire. The measurements consisted of questionnaires and 7-day online diaries. The questionnaires measured insomnia severity, dysfunctional beliefs about sleep, and anxiety and depression symptoms. The diary measured sleep variables such as sleep efficiency. We performed multilevel analyses to study the interaction effects between time and condition.ResultsThe results showed significant interaction effects (P<.01) favoring the app condition on the primary outcome measures of insomnia severity (d=–0.66) and sleep efficiency (d=0.71). Overall, these improvements were also retained in a 3-month follow-up.ConclusionsThis study demonstrated the efficacy of a fully automated mobile phone app in the treatment of relatively mild insomnia. The effects were in the range of what is found for Web-based treatment in general. This supports the applicability of such technical tools in the treatment of insomnia. Future work should examine the generalizability to a more diverse population. Furthermore, the separate components of such an app should be investigated. It remains to be seen how this app can best be integrated into the current health regimens.Trial RegistrationNetherlands Trial Register: NTR5560; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5560 (Archived by WebCite at http://www.webcitation.org/6noLaUdJ4)
Expressive behaviour is a vital aspect of human interaction. A model for adaptive emotion expression was developed for the Nao robot. The robot has an internal arousal and valence value, which are influenced by the emotional state of its interaction partner and emotional occurrences such as winning a game. It expresses these emotions through its voice, posture, whole body poses, eye colour and gestures. An experiment with 18 children (mean age 9) and two Nao robots was conducted to study the influence of adaptive emotion expression on the interaction behaviour and opinions of children. In a within-subjects design the children played a quiz with both an affective robot using the model for adaptive emotion expression and a non-affective robot without this model. The affective robot reacted to the emotions of the child using the implementation of the model, the emotions of the child were interpreted by a Wizard of Oz. The dependent variables, namely the behaviour and opinions of the children, were measured through video analysis and questionnaires. The results show that children react more expressively and more positively to a robot which adaptively expresses itself than to a robot which does not. The feedback of the children in the questionnaires further suggests that showing emotion through movement is considered a very positive trait for a robot. From their positive reactions we can conclude that children enjoy interacting with a robot which adaptively expresses itself through emotion and gesture more than with a robot which does not do this.
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