用临床心电图模型适配可穿戴设备，实现无标签下的认知负荷实时评估
CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
- 通过可学习的导联映射层将可穿戴3导联信号转为12导联兼容表示
- 渐进式微调策略使模型在跨被试测试中宏平均F1达0.768，提升16.1个百分点
- 适合研究人机交互、智能可穿戴设备与医疗计算交叉领域的开发者
连续低延迟评估认知负荷有助于自适应人机交互，但受限于标注数据稀缺和模型跨被试泛化能力差。尽管近期基于百万级临床心电图数据预训练的诊断模型已取得进展，但在传感器配置和任务均不同的可穿戴场景下无法直接应用。本文提出CogAdapt框架，包含两部分：LeadBridge是可学习的适配器，将3导联可穿戴信号映射至12导联兼容表征；ProFine是渐进式微调策略，在逐步解冻编码器层的同时限制预训练模型的表征漂移。在两个公开数据集（CLARE和CL-Drive）上采用留一被试交叉验证，CogAdapt分别取得0.626和0.768的宏平均F1，较从零开始训练基线分别提升11.2和16.1个百分点。结果表明，临床心电图预训练可支持基于可穿戴传感器的跨被试认知负荷评估。
原文摘要 · Abstract (English)
Assessing cognitive load continuously and at low latency would help adaptive human-computer interaction, but it remains hard because labeled data are scarce and models generalize poorly across subjects. Recent ECG foundation models, pre-trained on millions of clinical diagnostic ECG recordings, yet they do not apply directly to wearable devices when the sensor configuration and the task both differ. We present CogAdapt, a framework that adapts a clinical ECG foundation model to wearable cognitive load assessment. CogAdapt has two parts. LeadBridge is a learnable adapter that maps 3-lead wearable signals to a 12-lead-compatible representation. ProFine is a progressive fine-tuning strategy that unfreezes encoder layers in stages while limiting representational drift in the pre-trained model. On two public datasets (CLARE and CL-Drive) under leave-one-subject-out cross-validation, CogAdapt reaches macro-F1 of 0.626 and 0.768, improving over from-scratch baselines by 11.2 and 16.1 percentage points. The results show that a clinical ECG pretraining can support subject-independent cognitive load assessment from wearable sensors.
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