Detecting nonverbal symptoms of schizophrenia and depression, utilising Computer Vision and Machine Learning methodologies

心理健康是全球残疾的主要原因之一，抑郁症是2010年全球残疾的第二大原因[1]。如果不及时治疗，可能会导致严重后果，往往是自杀[2]。尽管心理健康意识在不断提高，但我们仍然看到诊断不足。诊断不足的主要原因之一，例如在紧张症中，是获得诊断和治疗。心理健康诊断不足或误诊的最重要因素是心理健康评估的偏差[4，5]。这表明需要对心理健康疾病进行自动化和一致的诊断。这种方法将有助于获得诊断，并确保其公正的方法。因此，随着其他生物医学领域的创新，过去几年来机器学习研究社区对精神疾病症状严重程度估计产生了兴趣。精神疾病可以表现为非语言症状，包括身体和头部运动，眼球运动和面部表情，如ECSI框架所述[6]。据观察，抑郁症患者避免眼神接触，微笑较少，强度较低，动作较慢[7]。同样，精神分裂症患者倾向于避免眼神接触，并表现出更多的闭眼[6]。强有力的非语言线索是创造一种自动方法来检测和评估精神疾病的另一个动机，因为它们很难用肉眼测量和记录;此外，治疗师很难客观地定义微妙的症状（或缺乏症状），从而在诊断中引入偏见。特别是对于行为症状，据估计，准确和客观地量化它们比填写评级量表要耗时十倍，因此，有关行为的重要信息会丢失或误解[6]。这项工作将专注于利用计算机视觉和机器学习方法检测精神分裂症和抑郁症的非语言症状。第一阶段将侧重于从个人模式，特别是身体姿势和运动的预测。然后，它将研究融合技术，将不同的模态与现有的人脸模态连接起来，并提高模型的整体预测精度。最后，它将研究可解释的人工智能技术，以在时间和空间上定位有助于系统预测的因素。[1]Alize J Ferrari，Fiona J Charlson，Rosana E Norman，Scott B Patten，GregFreedman，Christopher JL Murray，Theo Vos，and Harvey A怀特福德，“国家、性别、年龄和年份的抑郁症负担：2010年全球疾病负担研究的结果“，PLoS medicine，vol. 10，no.11，pp. e1001547，2013年。[2]欧洲联盟委员会，“改善人口的心理健康：制定欧洲联盟心理健康战略”，报告，欧洲共同体斯洛文尼亚，2005年。[3]K Adorjan，P Falkai，and O Pogarell，“Catatonia in clinical reality：underdiagnosed and forgotten，“MMW Fortschritte der Medizin，vol. 161，no. Suppl 7，pp. 2019年7月。[4]朗尼·R·斯诺登，“心理健康评估和干预的偏见：理论和证据“，《美国公共卫生杂志》，第93卷，第2期，第100页。239-243，2003. [5]Barbel Knaper和Hans-Ulrich Wittchen，“老年人抑郁症的诊断：标准化诊断访谈中反应偏差的证据？，”《精神病学研究杂志》，第28卷，第2期，第100页。147-164，1994年。[6]阿方索·特罗伊西，“临床精神病学中的行为学研究：访谈中的非语言行为研究“，《神经科学与生物行为评论》，第23卷，第7期，第110页。905-913，1999年。[7]Stefan谢勒，Giota Stratou，Marwa Mahmoud，Jill Boberg，Jonathan Gratch，Albert Rizzo，and Louis-Philippe Mohammed，“Automatic behavior descriptors for psychological disorder analysis，”in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Aberration（FG）. IEEE，2013年，第1-8.

Mental health is one of the leading causes of disability worldwide with depression being the second largest cause of disability worldwide in 2010 [1]. If left untreated it can lead to serious consequences often suicide [2]. Even though mental health awareness is on the rise we still do see underdiagnosis.One of the major contributors to underdiagnosis, for example in catatonia, is access to diagnosis and treatment [3]. The most significant factor in mental health underdiagnosis or misdiagnosis is bias in mental health assessment [4, 5]. These show a need for automated and consistent diagnosis for mental health illnesses. Such an approach would facilitate access to diagnosis as well as ensure an unbiased approach to it. As such, the past few years have seen interest from the machine learning research community in mental illness symptom severity estimation, following innovation in other biomedical fields.Mental illness can manifest with non-verbal symptoms, including body and head movement, eye movement and facial expressions as mentioned by ECSI framework [6]. Patients with depression have been observed to avoid eye contact, smile less and with lower intensity and slower movement [7]. Similarly, patients with schizophrenia tend to avoid eye contact and show more eye closures [6]. Strong nonverbal cues are an additional motivation for the creation of an automatic methodology to detect and assess mental illness as they are harder to measure and record with naked eye; furthermore subtle symptoms (or the lack of symptoms) are harder for therapists to objectively define, introducing bias in diagnosis. Specifically for behavioural symptoms it is estimated that accurately and objectively quantifying them is ten times more time-consuming than filling a rating scale, therefore important information around behaviour is lost or misinterpreted [6].This work will focus on detecting nonverbal symptoms of schizophrenia and depression, utilising Computer Vision and Machine Learning methodologies. The first stage will focus on predictions from individual modalities, specifically body pose and movement. It will then investigate fusion techniques to connect the different modalities with existing face modality and improve overall prediction accuracy of the model. Finally, it will look into explainable AI techniques to temporally and spatially localise the factors that contribute to the systems prediction.[1] Alize J Ferrari, Fiona J Charlson, Rosana E Norman, Scott B Patten, GregFreedman, Christopher JL Murray, Theo Vos, and Harvey A Whiteford,"Burden of depressive disorders by country, sex, age, and year: findingsfrom the global burden of disease study 2010,"PLoS medicine, vol. 10, no.11, pp. e1001547, 2013.[2] European Commission, "Improving the mental health of the population:Towards a strategy on mental health for the European Union," Report,European Communities Bruselas, 2005.[3] K Adorjan, P Falkai, and O Pogarell, "Catatonia in clinical reality: underdiagnosed and forgotten,"MMW Fortschritte der Medizin, vol. 161, no.Suppl 7, pp. 7, 2019.[4] Lonnie R Snowden, "Bias in mental health assessment and intervention:Theory and evidence,"American Journal of Public Health, vol. 93, no. 2,pp. 239-243, 2003.[5] Barbel Knauper and Hans-Ulrich Wittchen, "Diagnosing major depression in the elderly: evidence for response bias in standardized diagnostic interviews?,"Journal of Psychiatric Research, vol. 28, no. 2, pp. 147-164, 1994.[6] Alfonso Troisi, "Ethological research in clinical psychiatry: the study of nonverbal behavior during interviews,"Neuroscience & Biobehavioral Reviews, vol. 23, no. 7, pp. 905-913, 1999.[7] Stefan Scherer, Giota Stratou, Marwa Mahmoud, Jill Boberg, Jonathan Gratch, Albert Rizzo, and Louis-Philippe Morency, "Automatic behavior descriptors for psychological disorder analysis," in2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recog-nition (FG). IEEE, 2013, pp. 1-8.