Boosting Cognitive Workload Estimation: Federated Learning, Client Personalisation, Multimodal Data

What is it about?

This study introduces a new method to estimate cognitive workload, which is vital in healthcare and human-machine interactions. Traditional methods face challenges due to diverse sensor data from different clients and privacy concerns. Our approach employs Federated Learning, a technique that keeps data private and secure, to develop a more robust and generalizable model. We also introduce a multimodal model capable of handling clients with varying numbers of sensors. Additionally, an Unsupervised Client Personalisation method customizes models for each new, unseen client, enhancing accuracy during test time.

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Why is it important?

This work is significant due to its innovative combination of Federated Learning with a multimodal model and Unsupervised Personalisation. It tackles major cognitive workload estimation challenges: preserving data privacy, managing diverse data sources, and ensuring high accuracy for new, unseen users by creating customized models. This timely approach addresses growing data privacy concerns in healthcare and AI, offering a model-agnostic framework potentially revolutionizing cognitive workload measurement and applicable to other models and tasks.

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