通过分析重度抑郁症患者访谈时的头部运动，精准预测焦虑水平。
Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population
- 基于头部运动速度、加速度和角度位移，建立非侵入式焦虑评估方法。
- 在新数据集上预测焦虑程度，平均误差仅0.35，精度高。
- 适合精神科医生辅助个性化治疗决策，尤其关注抑郁伴焦虑人群。
抑郁症与焦虑症常共病，焦虑显著影响抑郁的表现与治疗。准确评估抑郁症患者的焦虑程度对制定有效个性化治疗方案至关重要。本研究提出一种新型非侵入性方法，通过分析严重抑郁症患者在视频访谈中的头部运动（包括速度、加速度和角位移）来量化焦虑严重程度。基于新构建的CALYPSO抑郁症数据集，提取头部运动特征并采用回归分析预测临床评估的焦虑水平。结果表明，该方法预测心理焦虑严重程度的平均绝对误差（MAE）为0.35，表现出高精度。这说明该方法有助于深入理解焦虑在抑郁中的作用，并支持精神科医生优化个体化治疗策略。
原文摘要 · Abstract (English)
Depression and anxiety are prevalent mental health disorders that frequently cooccur, with anxiety significantly influencing both the manifestation and treatment of depression. An accurate assessment of anxiety levels in individuals with depression is crucial to develop effective and personalized treatment plans. This study proposes a new noninvasive method for quantifying anxiety severity by analyzing head movements -- specifically speed, acceleration, and angular displacement -- during video-recorded interviews with patients suffering from severe depression. Using data from a new CALYPSO Depression Dataset, we extracted head motion characteristics and applied regression analysis to predict clinically evaluated anxiety levels. Our results demonstrate a high level of precision, achieving a mean absolute error (MAE) of 0.35 in predicting the severity of psychological anxiety based on head movement patterns. This indicates that our approach can enhance the understanding of anxiety's role in depression and assist psychiatrists in refining treatment strategies for individuals.
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