Head Pose and Movement Analysis as an Indicator of Depression

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.

I. Introduction

Clinical depression, unlike mood fluctuation, is a common mental disorder that lasts longer and causes disability and reduced functionality. A recent World Health Organization (WHO, 2012) survey estimated that 350 million people worldwide are affected by depression. With increased severity level, it might lead to suicide. Moreover, it causes more than two-thirds of suicides each year [1]. The suicide risk is more than 30 times higher among depressed patients than that of the population without these disorders [2]. Although treatment of depression disorders has proven to be effective in most cases [3], misdiagnosing depressed patients is a common barrier [4]. Moreover, the assessment methods of diagnosing depression rely almost exclusively on patient-reported or clinical judgments of symptom severity [5], risking a range of subjective biases. Based on the WHO, the barriers to effective diagnosis of depression include a lack of resources and trained health care providers. Our goal here is to investigate the general characteristics of depression, which we hope will lead to an objective affective sensing system that assists clinicians in their diagnosis and monitoring of clinical depression. Ultimately, we hope to assist patients with depression to monitor the progress of their illness.