Head Pose and Movement Analysis as an Indicator of Depression

Sharifa Alghowinem, Roland GOECKE, Michael WAGNER, Gordon Parker, Michael Breakspear

Research output: A Conference proceeding or a Chapter in BookConference contribution

38 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction
EditorsThierry Pun, Catherine Pelachaud, Nicu Sebe
Place of PublicationGeneva, Switzerland
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages283-288
Number of pages6
ISBN (Electronic)9780769550480
DOIs
Publication statusPublished - 2013
EventFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction: ACII 2013 - Emotion, Technology, Humanities - Geneva, Geneva, Switzerland
Duration: 2 Sep 20135 Sep 2013
http://Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (Conference Link)

Conference

ConferenceFifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction
Abbreviated titleACII 2013
CountrySwitzerland
CityGeneva
Period2/09/135/09/13
Internet address

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Head Movements
Depression
Emotions
Cues
Asthenopia
Expressed Emotion
Mental Disorders
Mental Health
Head

Cite this

Alghowinem, S., GOECKE, R., WAGNER, M., Parker, G., & Breakspear, M. (2013). Head Pose and Movement Analysis as an Indicator of Depression. In T. Pun, C. Pelachaud, & N. Sebe (Eds.), Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (pp. 283-288). Geneva, Switzerland: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACII.2013.53
Alghowinem, Sharifa ; GOECKE, Roland ; WAGNER, Michael ; Parker, Gordon ; Breakspear, Michael. / Head Pose and Movement Analysis as an Indicator of Depression. Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. editor / Thierry Pun ; Catherine Pelachaud ; Nicu Sebe. Geneva, Switzerland : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 283-288
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Alghowinem, S, GOECKE, R, WAGNER, M, Parker, G & Breakspear, M 2013, Head Pose and Movement Analysis as an Indicator of Depression. in T Pun, C Pelachaud & N Sebe (eds), Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, Institute of Electrical and Electronics Engineers, Geneva, Switzerland, pp. 283-288, Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2/09/13. https://doi.org/10.1109/ACII.2013.53

Head Pose and Movement Analysis as an Indicator of Depression. / Alghowinem, Sharifa; GOECKE, Roland; WAGNER, Michael; Parker, Gordon; Breakspear, Michael.

Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. ed. / Thierry Pun; Catherine Pelachaud; Nicu Sebe. Geneva, Switzerland : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 283-288.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Head Pose and Movement Analysis as an Indicator of Depression

AU - Alghowinem, Sharifa

AU - GOECKE, Roland

AU - WAGNER, Michael

AU - Parker, Gordon

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PY - 2013

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N2 - 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 andhealthy subjects, and could be used as a complementary cue.

AB - 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 andhealthy subjects, and could be used as a complementary cue.

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M3 - Conference contribution

SP - 283

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BT - Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction

A2 - Pun, Thierry

A2 - Pelachaud, Catherine

A2 - Sebe, Nicu

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Geneva, Switzerland

ER -

Alghowinem S, GOECKE R, WAGNER M, Parker G, Breakspear M. Head Pose and Movement Analysis as an Indicator of Depression. In Pun T, Pelachaud C, Sebe N, editors, Fifth Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction. Geneva, Switzerland: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 283-288 https://doi.org/10.1109/ACII.2013.53