We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions. We leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person's level of engagement in a semi-supervised GAN-based framework. We showcase the efficacy of this method from the perspective of mental health and more specifically how this can be leveraged for a better understanding of patient engagement during telemental health sessions. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists.

翻译：本文讨论了MET算法，这是一种基于学习的算法，旨在感知患者在远程医疗会话中的参与程度。我们利用心理学文献中常用于理解个体参与程度的情感与认知特征的潜在向量，在半监督生成对抗网络框架下进行分析。我们从心理健康角度展示了该方法的有效性，并具体说明了如何利用该方法更好地理解远程心理健康咨询中的患者参与程度。为促进类似技术在远程医疗领域的应用，我们还计划发布包含1299个3秒视频片段的MEDICA数据集，并展示了在该数据集上的实验结果。我们的框架在参与度评估中相较于现有最优方法，均方根误差降低了40%。在实际测试中，我们还观察到该结果与心理治疗师报告的工作联盟问卷得分呈正相关，表明所提出的模型能够提供与心理治疗师使用的参与度指标高度一致的患者参与度评估。