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Automatic Emotion Detection (ED) aims to build systems to identify users'emotions automatically. This field has the potential to enhance HCI, creatingan individualised experience for the user. However, ED systems tend to performpoorly on people with Autism Spectrum Disorder (ASD). Hence, the need to createED systems tailored to how people with autism express emotions. Previous workshave created ED systems tailored for children with ASD but did not share theresulting dataset. Sharing annotated datasets is essential to enable thedevelopment of more advanced computer models for ED within the researchcommunity. In this paper, we describe our experience establishing a process tocreate a multimodal annotated dataset featuring children with a level 1diagnosis of autism. In addition, we introduce CALMED (Children, Autism,Multimodal, Emotion, Detection), the resulting multimodal emotion detectiondataset featuring children with autism aged 8-12. CALMED includes audio andvideo features extracted from recording files of study sessions withparticipants, together with annotations provided by their parents into fourtarget classes. The generated dataset includes a total of 57,012 examples, witheach example representing a time window of 200ms (0.2s). Our experience andmethods described here, together with the dataset shared, aim to contribute tofuture research applications of affective computing in ASD, which has thepotential to create systems to improve the lives of people with ASD.

Introducing CALMED: Multimodal Annotated Dataset for Emotion Detection in Children with Autism