Multimodal wearable physiological data in daily life have been used to estimate self-reported stress labels. However, missing data modalities in data collection makes it challenging to leverage all the collected samples. Besides, heterogeneous sensor data and labels among individuals add challenges in building robust stress detection models. In this paper, we proposed a modality fusion network (MFN) to train models and infer self-reported binary stress labels under both complete and incomplete modality conditions. In addition, we applied personalized attention (PA) strategy to leverage personalized representation along with the generalized one-size-fits-all model. We evaluated our methods on a multimodal wearable sensor dataset (N=41) including galvanic skin response (GSR) and electrocardiogram (ECG). Compared to the baseline method using the samples with complete modalities, the performance of the MFN improved by 1.6% in f1-scores. On the other hand, the proposed PA strategy showed a 2.3% higher stress detection f1-score and approximately up to 70% reduction in personalized model parameter size (9.1 MB) compared to the previous state-of-the-art transfer learning strategy (29.3 MB).

翻译：在日常生活中,使用多式磨损生理数据来估计自我上报的压力标签。然而,数据收集中缺少的数据模式使得利用所有收集的样本具有挑战性。此外,不同传感器数据和个人标签在建立稳健的压力检测模型方面增加了挑战。在本文件中,我们提议了一个模式融合网络(MFN),在完整和不完整的模式条件下,对模型进行培训,并推断自我上报的二元压力标签;此外,我们采用了个性化关注战略(PA),在通用的一刀切模式下,利用个性化代表模式。我们评估了多式可磨损传感器数据集(N=41)的方法,包括变色皮肤反应(GSR)和电动心电图(ECG)。与使用完整模式样本的基线方法相比,最惠国在F1核心中的表现提高了1.6%。另一方面,拟议的PA战略显示,与先前的州转移学习战略(29.3MB)相比,压力检测F1核心和二元值增加了2.3%,个人化模型的参数大小大约减少了70%(9.1MB)。