Foundation models trained on large clinical datasets can be effectively adapted to wearable sensor tasks through domain-specific adapters and careful fine-tuning, enabling better cognitive load assessment with limited labeled data.
CogAdapt adapts pre-trained clinical ECG models to assess cognitive load from wearable devices. It uses a learnable adapter to convert 3-lead wearable signals into 12-lead clinical format and a progressive fine-tuning strategy to preserve learned knowledge while adapting to the new task, achieving strong performance on cognitive load prediction.