Interpersonal conflict between couples is a significant source of stress with long-lasting effects on partners’ physical and psychological health. Motivated by findings in psychological science, we study how couples with distinct relationship functioning characteristics experience conflict in real life. We propose sub-population specific machine learning models using hierarchical and adaptive learning frameworks to automatically detect interpersonal conflict through the ambulatory monitoring of couples’ physiological signals, audio samples, and linguistic indices. Results indicate that the proposed models outperform a general model learned for the entire population and separate models independently trained on each sub-population, providing a foundation toward personalized health applications.

中文翻译：

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夫妻冲突的亚人群特定模型

夫妻之间的人际冲突是压力的重要来源，对伴侣的身心健康具有长期影响。受心理科学发现的启发，我们研究了具有不同关系功能特征的夫妻在现实生活中如何经历冲突。我们提出了使用分层和自适应学习框架的子人群特定机器学习模型，通过动态监测夫妻的生理信号、音频样本和语言指标来自动检测人际冲突。结果表明，所提出的模型优于为整个人群学习的通用模型，并且在每个子人群上独立训练的单独模型，为个性化健康应用奠定了基础。