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Lundi 12 Février
| Heure: |
12:15 - 13:00 |
| Lieu: |
Salle B107, bâtiment B, Université de Villetaneuse |
| Résumé: |
Ethically-driven Multimodal Emotion Detection for Children with Autism |
| Description: |
Annanda Sousa Emotion detection (ED) aims to identify peoples emotions automatically. However, most ED
applications do not consider individuals who express emotions differently, such as people with
autism. Although studies have already focused on creating ED models tailored for children with
ASD, this application of ED suffers from a scarcity of resources and remains underperforming
compared to the state-of-the-art ED models for the general population.
This thesis addresses the gap in automatic ED between the general population and autistic
children while ensuring an ethically driven approach, i.e., having the well-being of participants
as the main priority during the whole research process.
To meet our research objectives, we created a data collection framework that minimises emo-
tional disruption to the participants, respects their privacy and rights according to GDPR, and
provides a dataset that can be shared with the research community. We created CALMED,
a multimodal annotated dataset for ED featuring children with autism that includes privacy-
preserving features, novel target emotion classes, annotations provided by the participants par-
ents and a researcher specialist who works with children with ASD.
Using the CALMED dataset, we created hundreds of models with unique configurations and
analysed them to explore the effectiveness of various methods for multimodal ED in autism.
Then, utilising the knowledge acquired in this analysis, we proposed a multimodal ED model
that outperformed the previous state-of-the-art, reaching 81.56% and 75.47% for accuracy and
balanced accuracy, respectively.
Finally, we created and shared many systems to support the data acquisition process and
data experiments creation and analysis. We placed great importance on ensuring reproducibility,
reusability, and ethical conduct.
This research has made significant contributions to the field of ED applied to ASD. It has
provided a valuable dataset, analytical insights, a state-of-the-art model, and many computer
systems that can serve as a groundwork for future work. |
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