  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).
