The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.

通过在治疗过程中跟踪抑郁症参与者并录制一系列临床访谈视频，研究非语言行为与抑郁症严重程度之间的关系。使用手动和自动系统从视频中分析面部表情和头部姿势。两个系统的 FACS 行动单位 (AU) 高度一致，并且对抑郁严重程度随时间变化的影响相似。当症状严重程度较高时，参与者会做出较少的亲和面部表情（AU 12 和 15），而会做出更多的非亲和面部表情（AU 14）。当症状严重程度较高时，参与者还表现出头部运动（即幅度和速度）减弱。这些结果与社交退缩假说一致：抑郁症患者使用非语言行为来维持或增加人际距离。随着个人康复，他们会发出更多信号表明愿意加入。自动面部表情分析与手动编码一致，并揭示了相同的结果模式，这一发现表明自动面部表情分析可能已经准备好减轻行为和临床科学中手动编码的负担。