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