Gram Matrices Formulation of Body Shape Motion: An Application for Depression Severity Assessment

International audienceWe propose an automatic method to measure depression severity from body movement dynamics in participants undergoing treatment for depression. Participants were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at seven-week intervals over a period of 21 weeks. Gram matrices formulation was used for body shape and trajectories representation from each video interview. Kinematic features were then extracted and encoded for video based representation using Gaussian Mixture Models (GMM) and Fisher vector encoding. A multi-class SVM was finally used to classify the encoded body movement dynamics into three levels of depression severity scores: moderate to severely depressed, mildly depressed, and remitted. Accuracy was higher for moderate to severe depression (68%) followed by mild depression (56%), and then remitted (37.93%). The obtained results suggest that automatic detection of depression severity from body movement is feasible

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Automatic Detection of Self-Adaptors for Psychological Distress

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the lack of available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We also propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector encoding. We also demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels. To enable this research we introduce a new dataset containing full body videos for short interviews and self-reported distress labels.King's College, Cmabridg

Explainable Depression Detection via Head Motion Patterns

While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed \emph{kinemes}, for depression detection by adopting two distinct approaches, and employing distinctive features: (a) discovering kinemes from head motion data corresponding to both depressed patients and healthy controls, and (b) learning kineme patterns only from healthy controls, and computing statistics derived from reconstruction errors for both the patient and control classes. Employing machine learning methods, we evaluate depression classification performance on the \emph{BlackDog} and \emph{AVEC2013} datasets. Our findings indicate that: (1) head motion patterns are effective biomarkers for detecting depressive symptoms, and (2) explanatory kineme patterns consistent with prior findings can be observed for the two classes. Overall, we achieve peak F1 scores of 0.79 and 0.82, respectively, over BlackDog and AVEC2013 for binary classification over episodic \emph{thin-slices}, and a peak F1 of 0.72 over videos for AVEC2013

Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the limited available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. To enable this research, we have collected and analyzed a new dataset containing full body videos for short interviews and self-reported distress labels. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We perform analysis on statistical body gestures and fidgeting features to explore how distress levels affect participants' behaviors. We then propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels

Applications of Multivariate Pattern Classification Analyses in Developmental Neuroimaging of Healthy and Clinical Populations

Analyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been gaining traction in neuroimaging of adult healthy and clinical populations; studies have shown that information present in neuroimaging data can be used to decode intentions and perceptual states, as well as discriminate between healthy and diseased brains. While few studies to date have applied these methods in pediatric populations, in this review we discuss exciting potential applications for studying both healthy, and aberrant, brain development. We include an overview of methods and discussion of challenges and limitations

When a few words are not enough: improving text classification through contextual information

Traditional text classification approaches may be ineffective when applied to texts with insufficient or limited number of words due to brevity of text and sparsity of feature space. The lack of contextual information can make texts ambiguous; hence, text classification approaches relying solely on words may not properly capture the critical features of a real-world problem. One of the popular approaches to overcoming this problem is to enrich texts with additional domain-specific features. Thus, this thesis shows how it can be done in two realworld problems in which text information alone is insufficient for classification. While one problem is depression detection based on the automatic analysis of clinical interviews, another problem is detecting fake online news. Depression profoundly affects how people behave, perceive, and interact. Language reveals our ideas, moods, feelings, beliefs, behaviours and personalities. However, because of inherent variations in the speech system, no single cue is sufficiently discriminative as a sign of depression on its own. This means that language alone may not be adequate for understanding a person’s mental characteristics and states. Therefore, adding contextual information can properly represent the critical features of texts. Speech includes both linguistic content (what people say) and acoustic aspects (how words are said), which provide important clues about the speaker’s emotional, physiological and mental characteristics. Therefore, we study the possibility of effectively detecting depression using unobtrusive and inexpensive technologies based on the automatic analysis of language (what you say) and speech (how you say it). For fake news detection, people seem to use their cognitive abilities to hide information, which induces behavioural change, thereby changing their writing style and word choices. Therefore, the spread of false claims has polluted the web. However, the claims are relatively short and include limited content. Thus, capturing only text features of the claims will not provide sufficient information to detect deceptive claims. Evidence articles can help support the factual claim by representing the central content of the claim more authentically. Therefore, we propose an automated credibility assessment approach based on linguistic analysis of the claim and its evidence articles