Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
This work addresses challenges in real-world stress detection for individuals using wearable sensors, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of detecting momentary stress in daily life using multimodal wearable data by proposing a modality fusion network to handle missing modalities and a personalized attention strategy to address individual heterogeneity, resulting in a 1.6% improvement in F1-score for modality fusion and a 2.3% higher F1-score with up to 70% parameter reduction for personalization.
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).