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Modern smartwatches or wrist wearables having multiple physiological sensing modalities have emerged as a subtle way to detect different mental health conditions, such as anxiety, emotions, and stress. However, affect detection models depending on wrist sensors data often provide poor performance due to inconsistent or inaccurate signals and scarcity of labeled data representing a condition. Although learning representations based on the physiological similarities of the affective tasks offer a possibility to solve this problem, existing approaches fail to effectively generate representations that will work across these multiple tasks. Moreover, the problem becomes more challenging due to the large domain gap among these affective applications and the discrepancies among the multiple sensing modalities. We present M3Sense, a multi-task, multimodal representation learning framework that effectively learns the affect-agnostic physiological representations from limited labeled data and uses a novel domain alignment technique to utilize the unlabeled data from the other affective tasks to accurately detect these mental health conditions using wrist sensors only. We apply M3Sense to 3 mental health applications, and quantify the achieved performance boost compared to the state-of-the-art using extensive evaluations and ablation studies on publicly available and collected datasets. Moreover, we extensively investigate what combination of tasks and modalities aids in developing a robust Multitask Learning model for affect recognition. Our analysis shows that incorporating emotion detection in the learning models degrades the performance of anxiety and stress detection, whereas stress detection helps to boost the emotion detection performance. Our results also show that M3Sense provides consistent performance across all affective tasks and available modalities and also improves the performance of representation learning models on unseen affective tasks by 5% - 60%. |
