Head Pose and Movement Analysis as an Indicator of Depression

Abstract

Abstract-Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also their families, friends and the economy overall. Our ultimate aim is to develop an automatic objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. Here, we analyse the performance of head pose and movement features extracted from face videos using a 3D face model projected on a 2D Active Appearance Model (AAM). In a binary classification task (depressed vs. non-depressed), we modelled low-level and statistical functional features for an SVM classifier using real-world clinically validated data. Although the head pose and movement would be used as a complementary cue in detecting depression in practice, their recognition rate was impressive on its own, giving 71.2% on average, which illustrates that head pose and movement hold effective cues in diagnosing depression. When expressing positive and negative emotions, recognising depression using positive emotions was more accurate than using negative emotions. We conclude that positive emotions are expressed less in depressed subjects at all times, and that negative emotions have less discriminatory power than positive emotions in detecting depression. Analysing the functional features statistically illustrates several behaviour patterns for depressed subjects: (1) slower head movements, (2) less change of head position, (3) longer duration of looking to the right, (4) longer duration of looking down, which may indicate fatigue and eye contact avoidance. We conclude that head movements are significantly different between depressed patients and healthy subjects, and could be used as a complementary cue.

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

“…Significant related work has been done on the automatic analysis and prediction of depression using speech and prosody [28,21,23,46], gestures, head pose, and facial expressions [2,33,43,19], and a multimodal combination of these cues [49,20,27,26]. As described in the literature, there are a variety of challenges in automatically determining whether a person is depressed, including: 1) each behavioral signal provides only partial information, which must be combined to form a more realistic model for recognizing behaviors indicative of depression; 2) some information that is relevant may not be available or may be inherently hidden; 3) defining baseline behavior can be difficult with limited behavioral data.…”

Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding

“…The latter use signal processing, computer vision, and pattern recognition methodologies. From the computer-science perspective, research has sought to identify depression from vocal utterances [8], [9], [10], [11], [12], [13], facial expression [14], [15], [16], [17], head movements/pose [18], [16], [19], body movements [18], and gaze [20]. While most research is limited to a single modality, there is increasing interest in multimodal approaches to depression detection [21], [22].…”

A temporally piece-wise fisher vector approach for depression analysis

“…For face analysis, the fiducial points detection is performed using the Intraface library 2 . Using the fiducial points, affine warp is computed, which gives the aligned faces.…”

“…Recently, [18], [2] also used head movements when analysing depression data. A histogram of head movements was proposed by [18], it is experimented of the Black Dog Institute depression data.…”

“…A histogram of head movements was proposed by [18], it is experimented of the Black Dog Institute depression data. In [2], AAM based head pose detection is performed and futher, statistics are computed. In an interesting work, [1] explored the effectiveness of eye analysis based features for depression detection of the Black Dog Institute data.…”