Assistive technologies can improve the quality of life of people diagnosed with different forms of social communication disorders. We report on the design and evaluation of an affective avatar aimed at engaging the user in a social interaction with the purpose of assisting in communication therapies. A human-avatar taxonomy is proposed to assist the design of affective avatars aimed at addressing social communication disorder. The avatar was evaluated with 30 subjects to assess how effectively it conveys the desired emotion and elicits empathy from the user. Results provide evidence that users become used to the avatar after a number of interactions, and they perceive the defined behavior as being logical. The users' interactions with the avatar entail affective reactions, including the mimic emotions that users felt, and establish a preliminary ground truth about prototypic empathic interactions with avatars that is being used to train learning algorithms to support social communication disorder evaluation.

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The study of cognitive responses and processes while using applications is a critical field in human-computer interaction. This paper aims to determine the mental effort required for different typical tasks with smartphones. Mental effort is typically associated with the concept of cognitive load, and has been studied by analyzing electroencephalography (EEG) signals. Thus, this paper shows the results of analyzing the cognitive load of a set of characteristic tasks on smartphones. To determine the set of tasks to analyze, this paper proposes a taxonomy of smartphone-based actions defined after considering the related proposals in the literature and identifying the significant characteristics of the tasks to classify them. The EEG data was obtained through an experiment with real users doing tasks from the aforementioned taxonomy. The results show significant differences in the cognitive load of each task category and identify those tasks that involve a higher degree of mental effort. The results will be the starting point of the M 4 S project that aims to contribute to the early diagnosis of mild cognitive impairment through monitoring everyday dual-tasking in terms of interaction with smartphones.

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