An affective system for early diagnosis of mental health disorders

辅助技术，康复和肌肉骨骼生物力学-医疗保健技术情感计算（AC）是一个研究领域，专注于设计和创建能够识别，处理和与人类情感互动的系统或设备。拟议的博士研究将调查使用AC技术诊断和监测心理健康障碍，特别是抑郁症。该研究将研究从面部表情和语音中自动识别情绪的方法，以及用于开发系统的情感分析方法，以帮助临床医生发现抑郁症的早期迹象，并在整个康复治疗过程中监测患者的情感状态。这项研究最初将使用现有的公开数据库，例如，SEMAINE数据集，并调整现有的和正在开发的机器学习方法，以建立受试者情感状态的预测模型。在系统开发的后期阶段，医生将参与系统性能的确认过程。博士研究的目的是设计和开发一个AC系统，它可以识别抑郁症的症状，以便早期采取纠正措施。该研究将在为系统选择/调整/开发最佳工具之前调查情感检测方法，情感分析和行为监测的适当性。使用的方式是面部表情和语音（口语和书面语）。博士研究的目标：研究涉及面部表情和语音的多模态情感识别方法，以识别心理健康障碍的早期症状。研究使用口头或书面文字的情感分析来推断受试者的心理状态。研究一种实时、准确的人脸表情分类方法。探讨在开发抑郁症AC监测系统时，将上述三个目标的结果结合起来。研究方法的新颖性：结合使用面部动作编码系统的动作单元（用于分类面部表情），声学特征（用于检测指示抑郁症或其他精神障碍的语音参数）和多尺度特征融合来检测抑郁症及其严重程度。使用情感分析和机器学习来生成用于识别文本或语音部分中表达的不同情感的模型。使用流形学习从面部表情中识别抑郁症。特征融合的优化过程。

Assistive technology, rehabilitation and musculoskeletal biomechanics - Healthcare technologiesAffective computing (AC) is a field of research focussed on the design and creation of systems or devices capable of recognising, processing, and interacting with human affect. The proposed PhD research will investigate the use of AC technologies for the diagnosis and monitoring of mental health disorders, in particular depression. The research will investigate the development of automated emotion recognition method/s from facial expression and speech, and a sentiment analysis method for the development of a system to help clinicians detect early sign of depression and monitor patients' affective state throughout the rehabilitative treatment. The research will initially use existing publicly available databases, e.g., SEMAINE dataset, and adapting existing and developing machine learning methods to build predictive models of a subject's affective state. The later stages of the system development will involve a physician in the validation process of the system performance. The aim of the PhD research is to design and develop an AC system which identifies symptoms of depression allowing for early corrective action. The research will investigate the appropriateness of affect detection methods, sentiment analysis and behavioural monitoring before selecting/adapting/developing the optimal tools for the system. The modalities to use are facial expressions and speech (spoken and written). The objectives of the PhD research: To investigate multimodal affect recognition approach involving facial expressions and speech to identify early symptoms of mental health disorders. To investigative the use of sentiment analysis of spoken or written words to infer the subject's mental state. To investigate a real-time and accurate method for classifying facial expressions. To investigative the incorporation of the outcome of the above three objectives in the development of an AC system for depression monitoring. Novelties of the research methodology: The combined use of action units of the Facial Action Coding System (for classifying facial expression), acoustic features (for detecting speech parameters that indicate depression or other mental disorders), and multiscale feature fusion to detect depression and their severity. The use of sentiment analysis and machine learning to generate a model for identifying different sentiment expressed in a section of text or speech. The use of manifold learning to recognise depression from facial expressions. The optimising procedure for feature fusion.