Automatic Multimodal Affect Detection for Research and Clinical Use

项目摘要 一个可靠和有效的自动化系统，用于量化人类在生态上重要的情感行为。 自然环境将是研究和临床实践的一种变革工具。关于NIMH 支持（MH R 01 -096951），我们已经朝着这一目标取得了根本性的进展。在项目中，我们 扩展当前情感行为（视觉、听觉和听觉）自动多模式测量的能力， 口头），以开发和验证一个自动化系统，用于检测积极，积极， 焦虑行为和低层次情感行为及言语内容的组成部分。该系统基于 《生活在家庭环境中的编码系统》手册，该手册产生了与以下方面有关的重要结论： 抑郁症和其他疾病的发展精神病理学和人际关系过程。两个模型 将被开发。一个将使用理论推导出的功能，由以前的研究，在行为 科学和情感计算;深度学习提供的其他经验性特征。的 模型将在三个独立的数据库中进行训练，这些数据库包含来自1300多个任务的二元和三元交互任务 来自美国和澳大利亚的青少年和成人参与者。 手动编码的系统间可靠性将使用k折交叉验证进行评估， 和会话级汇总分数。模型之间以及与参与者因素有关的差异将 使用一般线性模型进行测试。为了确保通用性，我们将进一步在 独立的数据库。为了评估自动编码的结构有效性，我们将使用示例 三个数据库中可用的有效性数据，以确定自动编码是否达到相同或 在抑郁症风险和发展方面有更好的发现模式。以下程序已经在 作为共享数据库和软件工具的地方，我们将设计供非专业人员使用的自动化系统 并将其用于研究和临床应用。实现这些目标将提供行为科学 具有强大的工具来检查情绪，精神病理学和人际关系过程中的基本问题; 临床医生改善评估和能力，以跟踪临床和人际功能随时间的变化。 相关性 对于行为科学，从多模态（视觉，听觉和言语）情感行为的自动编码 输入将为研究人员提供强大的工具，以检查情绪，精神病理学， 和人际交往过程。对于临床使用，自动化测量将帮助临床医生评估 脆弱性和保护因素以及对各种疾病的治疗反应。更一般地说， 自动化测量将有助于在教育，社会培训， 技能和说服力，以及更广泛的情感计算。

Project Summary A reliable and valid automated system for quantifying human affective behavior in ecologically important naturalistic environments would be a transformational tool for research and clinical practice. With NIMH support (MH R01-096951), we have made fundamental progress toward this goal. In the proposed project, we extend current capabilities in automated multimodal measurement of affective behavior (visual, acoustic, and verbal) to develop and validate an automated system for detecting the constructs of Positive, Aggressive, and Dysphoric behavior and component lower-level affective behaviors and verbal content. The system is based on the manual Living in Family Environments Coding System that has yielded critical findings related to developmental psychopathology and interpersonal processes in depression and other disorders. Two models will be developed. One will use theoretically-derived features informed by previous research in behavioral science and affective computing; the other empirically derived features informed by Deep Learning. The models will be trained in three separate databases of dyadic and triadic interaction tasks from over 1300 adolescent and adult participants from the US and Australia. Intersystem reliability with manual coding will be evaluated using k-fold cross-validation for both momentary and session level summary scores. Differences between models and in relation to participant factors will be tested using the general linear model. To ensure generalizability, we further will train and test between independent databases as well. To evaluate construct validity of automated coding, we will use the ample validity data available in the three databases to determine whether automated coding achieves the same or better pattern of findings with respect to depression risk and development. Following procedures already in place for sharing databases and software tools, we will design the automated systems for use by non-specialists and make them available for research and clinical use. Achieving these goals will provide behavioral science with powerful tools to examine basic questions in emotion, psychopathology, and interpersonal processes; and clinicians to improve assessment and ability to track change in clinical and interpersonal functioning over time. Relevance For behavioral science, automated coding of affective behavior from multimodal (visual, acoustic, and verbal) input will provide researchers with powerful tools to examine basic questions in emotion, psychopathology, and interpersonal processes. For clinical use, automated measurement will help clinicians to assess vulnerability and protective factors and response to treatment for a wide range of disorders. More generally, automated measurement would contribute to advances in intelligent tutors in education, training in social skills and persuasion in counseling, and affective computing more broadly.