CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
arXiv:2605.22774v1 Announce Type: new Abstract: Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cross-subject generalization. Recent ECG foundation models pre-trained on millions of clinical recordings offer rich representations, but cannot be directly applied to wearable devices due to sensor configuration mismatch and task differences. In this paper, we propose CogAdapt, a framework that adapts clinical ECG foundation models to wearable cognitive load assessment. CogAdapt introduces LeadBrid
The proliferation of wearable devices, coupled with advancements in AI model transfer learning, makes real-time cognitive load assessment a rapidly developing field.
This development can significantly enhance adaptive human-computer interaction and personalized health monitoring, moving beyond general physiological data to cognitive states.
Clinical ECG foundation models can now be effectively adapted for wearable devices to assess cognitive load, bridging the gap between medical-grade data and consumer-level applications.
- · Wearable device manufacturers
- · AI healthcare companies
- · Human-computer interaction researchers
- · Personalized health platforms
- · Traditional cognitive assessment methods
- · Companies without strong AI/ML integration
Increased accuracy and utility of wearable devices for health and performance monitoring.
New applications for cognitive load assessment in fields like education, professional training, and high-stress occupations.
The creation of hyper-personalized adaptive interfaces that proactively adjust to a user's mental state, potentially blurring lines between human and machine cognition.
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