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Exploring Contextual Engagement for Trauma Recovery

机译：探索创伤恢复的背景参与

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A wide range of research has used face data to estimate a person's engagement, in applications from advertising to student learning. An interesting and important question not addressed in prior work is if face-based models of engagement are generalizable and context-free, or do engagement models depend on context and task. This research shows that context-sensitive face-based engagement models are more accurate, at least in the space of web-based tools for trauma recovery. Estimating engagement is important as various psychological studies indicate that engagement is a key component to measure the effectiveness of treatment and can be predictive of behavioral outcomes in many applications. In this paper, we analyze user engagement in a trauma-recovery regime during two separate modules/tasks: relaxation and triggers. The dataset comprises of 8M+ frames from multiple videos collected from 110 subjects, with engagement data coming from 800+ subject self-reports. We build an engagement prediction model as sequence learning from facial Action Units (AUs) using Long Short Term Memory (LSTMs). Our experiments demonstrate that engagement prediction is contextual and depends significantly on the allocated task. Models trained to predict engagement on one task are only weak predictors for another and are much less accurate than context-specific models. Further, we show the interplay of subject mood and engagement using a very short version of Profile of Mood States (POMS) to extend our LSTM model.
机译：广泛的研究使用面部数据来估计一个人的参与，从广告到学生学习。未在事后工作未解决的有趣和重要的问题是，如果基于面部的参与模式是概括和无与伦比的，或者订婚模型取决于上下文和任务。本研究表明，基于背景基于面部的参与模型更准确，至少在基于Web的Trauma恢复工具的空间中。随着各种心理学研究表明，参与是测量治疗有效性的关键部件，并且可以预测许多应用中的行为结果可以预测行为结果是重要的。在本文中，我们在两个单独的模块/任务期间分析了创伤恢复制度的用户参与：放松和触发器。数据集包括从110个科目收集的多个视频的8M +帧，其中接合数据来自800+科目的自我报告。我们使用长短期存储器（LSTMS）从面部动作单位（AU）的序列学习接合预测模型。我们的实验表明，参与预测是语境的，并在分配的任务上显着取决于依赖。培训的模型预测一个任务的接触只是另一个任务的弱预测因子，并且比特定于上下文的模型更低。此外，我们展示了主题情绪和参与的相互作用，使用非常简短的情绪状态（POMS）的简介来扩展我们的LSTM模型。

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