Automated assessment of dyadic interaction using physiological synchrony and machine learning

使用生理同步和机器学习自动评估二元相互作用

• 批准号: 10553246
• 负责人: Vesna Dominika Novak
• 金额: $ 7.72万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10553246
• 关键词: Acting Out; Algorithms; Brain; Categories; Classification; Client; Clinical Psychology; Collaborations; Communication; Communities; Complement; Computers; Data; Data Set; Disadvantaged; Dryness; Education; Electrocardiogram; Electroencephalography; Emotional; Evaluation; Family; Foundations; Galvanic Skin Response; Goals; Health; Heart Rate; Human; Individual; Interpersonal Relations; Intervention; Machine Learning; Measurement; Measures; Mental Health; Methods; Outcome; Participant; Patient Self-Report; Pattern Recognition; Performance; Personal Communication; Personal Satisfaction; Persons; Physiological; Physiology; Psyche structure; Public Health; Quality of life; Research; Research Personnel; Research Project Grants; Respiration; Side; Signal Transduction; Skin Temperature; Social Functioning; Social Interaction; Source; Techniques; Technology; Testing; Time; automated analysis; classification algorithm; conflict resolution; dyadic interaction; improved; innovative technologies; insight; machine learning algorithm; member; mental health counseling; multidisciplinary; regression algorithm; response; sensor; signal processing; social; social engagement; social relationships; statistics; student participation; teacher; tool; verbal

ABSTRACT Interpersonal communication is critical for human health and wellbeing in situations such as mental health intervention, education, and conflict resolution. However, assessment of communication quality and social connectedness continues to rely on self-report measures and subjective observations. A more objective and dynamic approach to the evaluation of interpersonal engagement could provide a useful complement to state- of-the-art methods. For example, alternative methods could allow researchers to better quantify the flow of social interaction, determine how different aspects of communication influence outcomes, and identify avenues for enhancing interpersonal communication. Furthermore, valid measures of social engagement could benefit related fields such as computer-supported collaboration, with potential broad impacts on human quality of life. Recent technological advances have enabled the study of physiological synchrony: a phenomenon in which the physiological responses of two individuals (e.g., heart rate, respiration) converge as the individuals interact. Synchronization occurs involuntarily and could provide rich information about the dynamics of interpersonal relationships. However, while studies have shown robust correlations between physiological synchrony and engagement at the group level, there has been practically no effort to use synchrony to assess engagement at the level of individual dyads. Thus, this project will develop and evaluate machine learning technologies that can automatically recognize mental/interpersonal states of individual dyads based on their physiological responses. The project will consist of two studies. In the first study, we will use regression algorithms to estimate dyadic engagement over 60-second intervals of a naturalistic 15-minute conversation. In the second study, we will then use classification algorithms to classify 4-minute acted conversation scenarios into one of 4 classes: positive two-sided, negative two-sided, and two one-sided conversation classes. Regression and classification represent two major families of machine learning techniques, each with advantages and disadvantages, and will thus be examined in complementary studies. For each study, five physiological measurements (electrocardiography, skin conductance, respiration, skin temperature, dry electroencephalography) will be collected from both members of the dyad to serve as the basis for regression and classification. Upon completion, the project will provide the research community with validated methods for extracting dyad- level information about interpersonal interaction from physiological measurements. This will pave the way for future research that could explore how physiology-based assessment could provide useful data in realistic scenarios (e.g., mental health intervention and education), how it could be combined with other techniques (e.g., self-report), and how it might be used to enhance interpersonal interaction. In the long term, automated analysis of physiological responses may become part of an efficient toolbox for analysis and enhancement of dyadic interaction, providing numerous benefits to human health, abilities, and well-being.

摘要 人际沟通对人类的健康和福祉至关重要，在精神健康等情况下 干预、教育和冲突解决。然而，对沟通质量和社交能力的评估 连通性继续依赖于自我报告的衡量标准和主观观察。一个更加客观和 对人际投入的动态评估方法可以为国家-- 最先进的方法。例如，替代方法可以让研究人员更好地量化社交网络的流动 互动，确定沟通的不同方面如何影响结果，并确定 加强人际沟通。此外，有效的社会参与措施可能会受益。 计算机支持的协作等相关领域，可能对人类生活质量产生广泛影响。 最近的技术进步使对生理同步的研究成为可能：一种现象，在这种现象中， 当两个个体相互作用时，两个个体的生理反应(例如心率、呼吸)收敛。 同步不自觉地发生，可以提供关于人际关系动态的丰富信息 两性关系。然而，尽管研究表明生理同步性和 在团队层面的敬业度方面，几乎没有使用同步来评估敬业度的努力。 单个二联体的水平。因此，该项目将开发和评估机器学习技术， 根据他们的生理反应自动识别个体二元体的心理/人际关系状态。 该项目将包括两项研究。在第一项研究中，我们将使用回归算法来估计并元 在60秒的时间间隔内进行15分钟的自然主义对话。在第二项研究中，我们将 使用分类算法将4分钟的对话场景分类为以下4个类别之一：积极 双面对话、消极双面对话和两节单面对话课。回归和分类代表 机器学习技术的两个主要家族，每一个都有优缺点，因此 在补充研究中进行了检验。对于每项研究，五项生理测量(心电图， 皮肤电导、呼吸、皮肤温度、干脑电)将从两者收集 二分体的成员作为回归和分类的基础。 该项目完成后，将为研究界提供经过验证的提取二联体的方法。 来自生理测量的关于人际互动的水平信息。这将为以下工作铺平道路 未来的研究可以探索基于生理学的评估如何在现实中提供有用的数据 场景(例如，心理健康干预和教育)，它如何与其他技术相结合(例如， 自我报告)，以及如何利用它来加强人际互动。从长远来看，自动化分析 可能成为分析和增强并元反应的有效工具箱的一部分 互动，为人类的健康、能力和福祉提供许多好处。