PDF(Pubmed)
Abstract：
Virtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour-Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.
摘要：
虚拟现实暴露疗法是一种认知行为疗法的方法，通过使疗法切实可行和具有成本效益来帮助治疗焦虑症。它还允许通过使用客观，持续的反馈。这种反馈可以使用生物传感器来获得，以收集生理信息，例如心率，皮肤电活动和额叶大脑活动。作为开发我们客观反馈框架的一部分，我们开发了一个虚拟现实适应完善的情感Stroop颜色字任务。我们使用这种适应来区分三种不同的焦虑水平：没有焦虑，轻度焦虑和严重焦虑。我们对年龄在18至65岁之间的29名参与者进行了环境测试。在分析和验证此环境之后，我们使用它创建了一个数据集,用于对指定的焦虑水平进行进一步的机器学习分类.要实时应用这些信息，我们所有的信息都是在虚拟现实中处理的。我们的卷积神经网络能够使用留一交叉验证以75％的准确率区分焦虑水平。这表明我们的系统可以准确区分不同的焦虑水平。
