Abstract Individuals with autism may experience higher stress and anxiety levels for longer periods than their neurotypical peers. Traditional techniques to provide stress relief and anxiety management include support, which reminds people of how to face stressful situations. Some technological proposals supporting the user include strategies for detecting and monitoring stress levels. In this research, we conducted an iterative user-centered study aiming to understand how young adults with autism deal with stress in real-life situations. We proposed a set of five design principles that will serve as guidelines to develop assistive anxiety management technology for individuals with autism. We then developed a set of low-fi prototypes and selected SATORI, a support tool composed of three interfaces, to help young adults with autism autonomously manage the anxiety caused by stressful situations in their daily life. We evaluated our proposed design principles using SATORI with eight young adults with autism. The results show a positive perception of the design principles on what SATORI is based on, as participants perceived that SATORI could help them in their daily life to manage stress and channel anxiety.

Abstract The coding of observational data is commonly used to analyse and evaluate human behaviours. The technique can help researchers inform the design and impact of, for example, an Ubicomp system by studying specific behaviours of interest. There are some tools that can alleviate the burden of observational coding, like those that help to collect and organise data, but can still be error‐prone and time‐consuming. Moreover, most of these tools lack automation, requiring intense human interaction. In order to mitigate these issues, computer vision (CV) and machine learning (ML) techniques could be used to automate observational coding, but little work has focused on analysing the feasibility of such an approach, with the goal of reducing the total coding time while maintaining accuracy. In this work, we address this question by proposing an automated approach for a real‐world case study and compare it to manual coding. The study is composed of 10 videos with an average duration of 17 min each, where the goal is to determine the attention of children with autism that participate in a neurofeedback therapy session. Each video was hand‐coded by three human observers to define the ground truth and to measure the manual coding time. Results show that it is feasible to automate the coding of observational behaviours and obtain a noticeable reduction in coding time, but with a slight loss in accuracy. Moreover, we illustrate that the best solution would be a hybrid approach, using a semi‐automated system that combines human expertise and ML predictions Keywords Observational studies, Coding behaviours, Automatic coding, Computer‐Vision, Machine Learning.

Multimodal and natural user interfaces offer an innovative approach to sensory integration therapies. We designed and developed SensoryPaint, a multimodal system that allows users to paint on a large display using physical objects, body-based interactions, and interactive audio. We evaluated the impact of SensoryPaint through two user studies: a lab-based study of 15 children with neurodevelopmental disorders in which they used the system for up to one hour, and a deployment study with four children with autism, during which the system was integrated into existing daily sensory therapy sessions. Our results demonstrate that a multimodal large display, using whole body interactions combined with tangible interactions and interactive audio feedback, balances children's attention between their own bodies and sensory stimuli, augments existing therapies, and promotes socialization. These results offer implications for the design of other ubicomp systems for children with neurodevelopmental disorders and for their integration into therapeutic interventions.

Mother caregivers of children with cancer are often unaware of how the tasks of caregiving interfere in their social relationships and lifestyle. This article explores how persuasive mobile technologies may promote the self-reflection and introspection of communication practices, emotions, and lifestyle. The article describes the design of EmotionMingle, a mobile system running in a situated display showing an ambient visualization using the metaphor of a tree to represent the status of an individual’s social network that, used in tandem with downloaded Facebook photographs, may help caregivers avoid social isolation. EmotionMingle also informs caregivers of how their emotions correlate with their communication practices and lifestyle. The results of a qualitative evaluation of the EmotionMingle prototype reveal that mother caregivers perceived it as useful and its visualizations as appropriate. The findings from this study reveal emergent practices of using mobile persuasive applications for self-reflection to discover wellness trends, possibly inducing behavior change.

MOSOCO is a mobile assistive application that uses augmented reality and the visual supports of a validated curriculum, the Social Compass, to help children with autism practice social skills in real-life situations. In this paper, we present the results of a seven-week deployment study of MOSOCO in a public school in Southern California with both students with autism and neurotypical students. The results of our study demonstrate that MOSOCO facilitates practicing and learning social skills, increases both quantity and quality of social interactions, reduces social and behavioral missteps, and enables the integration of children with autism in social groups of neurotypical children. The findings from this study reveal emergent practices of the uses of mobile assistive technologies in real-life situations.