Introducing CALMED: Multimodal Annotated Dataset for Emotion Detection in Children with Autism
Annanda Sousa (NUI Galway), Karen Young (NUI Galway), Mathieu D'aquin, (Data Science, Knowledge, Reasoning, Engineering, LORIA, LORIA - NLPKD),, Manel Zarrouk (LIPN), Jennifer Holloway (ASK)

TL;DR
This paper presents CALMED, a multimodal annotated dataset of children with autism for emotion detection, aiming to improve personalized HCI systems and facilitate research in affective computing for ASD.
Contribution
The paper introduces CALMED, a new multimodal dataset with audio, video, and annotations for emotion detection in children with autism, sharing it to advance research.
Findings
Created a dataset with 57,012 examples of children with autism.
Collected multimodal data including audio, video, and annotations.
Shared the dataset to support future affective computing research.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
