title: Developing real time technology to support autistic emotion regulation:
title: an interdisciplinary exploration
titleType: Subtitle
Creators: name: Gillies-Walker, Lauren Margaret
Publisher: name: Zenodo
Year: 2025

description: As technology advances in the modern era, the potential for emotion regulation technology based on physiological signals to support physical and mental health is becoming increasingly popular. Technologies that support emotional self-awareness (e.g. brain-computer interface devices, wireless monitoring and mobile apps) may help individuals manage their mental health, leading to improvements in overall health and well-being. This could be particularly beneficial for Autistic people who report high levels of anxiety. Whilst this emerging area of research is exciting and innovative, real time emotion recognition from physiological signals is an extremely challenging and complex task, with many cognitive, affective, and environmental factors yet to be investigated within the literature. To date, there has been no qualitative exploration consulting the Autistic community to identify experiences and expectations of such technologies. Furthermore, despite a growing body of studies showing promise, the accuracy and reliability of such technology has been debated in the literature and there is currently no consensus for a universal method for affect identification. In order to gain insight into current developments, a systematic review of intervention studies was carried out, focusing on digital technologies (mobile applications, robotics, computer based/games and virtual reality) that specifically aim to support Autistic socio-emotional communication. Five databases were used to search for articles published between 2014-2022, resulting in 28 studies that met the inclusion criteria. Results provide evidence that, as technology advances, there is increasing potential for the use of technology to support Autistic socio-emotional communication. However, challenges are also highlighted (e.g many studies were based on outdated theoretical models, lack of robust methodology/objective measures, lack of follow up evaluation methods or co-participatory methods). Findings emphasise the need for technology to be developed and implemented alongside the Autistic community. To this end, focus groups with Autistic people and their allies (parents of Autistic people, health-care professionals and educational staff) were conducted to explore a wide range of experiences, expectations and opinions on technologies based on physiological signals. Thirty-four participants were recruited in total and Reflexive Thematic Analysis generated three themes: (1) Communication Challenges (2) Views on Emotion Regulation Technology (3) ‘How’ technology is implemented.Results provide meaningful insight into the socio-emotional communication challenges faced by Autistic people, and explore the expectations of technology aimed at supporting emotion regulation.These findings further our understanding of how such supports may improve overall well-being and quality of life. Ethical considerations that arose within the focus groups are also explored and aconceptual framework based on a thematic map of the focus group data is presented. This provides a basis for exploring the ethics of autonomous technologies based on physiological signals (EEG) that aim to support Autistic emotion regulationIn the quantitative phase of the thesis, machine learning methods are used to provide insight into both the neurophysiological basis underlying emotion processing and the feasibility of EEG techniques for the detection of affective states, ultimately providing a basis for automatic emotion regulation in therapeutic support strategies. The aim was to provide an indication of accuracy for affect identification within Autistic and non-Autistic populations. Results from three separate classifiers are reported for each group (Autistic and non-Autistic, Autistic only, non-Autistic only). In the Autistic and Non-Autistic groups which included all participants, Support Vector Machine with Gaussian Kernel produced the highest accuracy rates (Arousal 66% and Valence 59%). In the Autistic group, Ensemble of Trees produced the highest accuracy rate (Arousal 71%) and SVM produced the highest accuracy rate for valence (70%). In the Non-Autistic group, Ensemble of Trees produced the highest accuracy rate for Arousal (70.7%) and SVM produced the highest accuracy rate for Valence (57%). Overall, results suggest that EEG signals have discernible patterns that can be classified to identify different affective states. Taken together, the empirical findings extend the literature in wearable technology research and provide a basis for investigating ethical considerations which must be prioritised if the area is to progress. Importantly, this thesis has practical implications for Autism researchers, technology developers, health and social care professionals and policy makers alike.