Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. Currently, the
process of assessing each partner's emotions is manual, time-intensive, and
costly. Despite the existence of works on emotion recognition among couples,
none of these works have used data collected from couples' interactions in
daily life. In this work, we collected 85 hours (1,021 5-minute samples) of
real-world multimodal smartwatch sensor data (speech, heart rate,
accelerometer, and gyroscope) and self-reported emotion data (n=612) from 26
partners (13 couples) managing diabetes mellitus type 2 in daily life. We
extracted physiological, movement, acoustic, and linguistic features, and
trained machine learning models (support vector machine and random forest) to
recognize each partner's self-reported emotions (valence and arousal). Our
results from the best models (balanced accuracies of 63.8% and 78.1% for
arousal and valence respectively) are better than chance and our prior work
that also used data from German-speaking, Swiss-based couples, albeit, in the
lab. This work contributes toward building automated emotion recognition
systems that would eventually enable partners to monitor their emotions in
daily life and enable the delivery of interventions to improve their emotional
well-being.

本研究旨在使用智能手表传感器数据和自我报告情绪数据，提取不同特征并训练机器学习模型实现情绪自动识别。最终结果表明，提取的模型可识别患者的情绪，该研究有望为构建自动化情绪识别系统和情绪监测提供参考。