Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices.

We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and the pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used for representing the trajectory of landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank.

Turning data into better mental health: Past, present, and future.

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment.

Corrigendum: Computer Mediated Automatic Detection of Pain-Related Behavior: Prospect, Progress, Perils.

[This corrects the article DOI: 10.3389/fpain.2021.

Computer mediated automatic detection of pain-related behavior: prospect, progress, perils.

Pain is often characterized as a fundamentally subjective phenomenon; however, all pain assessment reduces the experience to observables, with strengths and limitations. Most evidence about pain derives from observations of pain-related behavior. There has been considerable progress in articulating the properties of behavioral indices of pain; especially, but not exclusively those based on facial expression.

Enforcing Multilabel Consistency for Automatic Spatio-Temporal Assessment of Shoulder Pain Intensity.

The standard clinical assessment of pain is limited primarily to self-reported pain or clinician impression. While the self-reported measurement of pain is useful, in some circumstances it cannot be obtained. Automatic facial expression analysis has emerged as a potential solution for an objective, reliable, and valid measurement of pain.

Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics.

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank.

Automated Assessment of Pain: Prospects, Progress, and a Path Forward.

Advances in the understanding and control of pain require methods for measuring its presence, intensity, and other qualities. Shortcomings of the main method for evaluating pain-verbal report-have motivated the pursuit of other measures. Measurement of observable pain-related behaviors, such as facial expressions, has provided an alternative, but has seen limited application because available techniques are burdensome.

Face and Gesture Analysis for Health Informatics.

The goal of Face and Gesture Analysis for Health Informatics's workshop is to share and discuss the achievements as well as the challenges in using computer vision and machine learning for automatic human behavior analysis and modeling for clinical research and healthcare applications. The workshop aims to promote current research and support growth of multidisciplinary collaborations to advance this groundbreaking research. The meeting gathers scientists working in related areas of computer vision and machine learning, multi-modal signal processing and fusion, human centered computing, behavioral sensing, assistive technologies, and medical tutoring systems for healthcare applications and medicine.

A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics.

Head movement is an important but often overlooked component of emotion and social interaction. Examination of regularity and differences in head movements of infant-mother dyads over time and across dyads can shed light on whether and how mothers and infants alter their dynamics over the course of an interaction to adapt to each others. One way to study these emergent differences in dynamics is to allow parameters that govern the patterns of interactions to change over time, and according to person- and dyad-specific characteristics.

Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews.

With few exceptions, most research in automated assessment of depression has considered only the patient's behavior to the exclusion of the therapist's behavior. We investigated the interpersonal coordination (synchrony) of head movement during patient-therapist clinical interviews. Our sample consisted of patients diagnosed with major depressive disorder.

Dynamics of Face and Head Movement in Infants with and without Craniofacial Microsomia: An Automatic Approach.

Background: Craniofacial microsomia (CFM) is a congenital condition associated with malformations of the bone and soft tissue of the face and the facial nerves, all of which have the potential to impair facial expressiveness. We investigated whether CFM-related variation in expressiveness is evident as early as infancy.

Methods: Participants were 113 ethnically diverse 13-month-old infants (n = 63 cases with CFM and n = 50 unaffected matched controls).

Detecting Depression Severity by Interpretable Representations of Motion Dynamics.

Recent breakthroughs in deep learning using automated measurement of face and head motion have made possible the first objective measurement of depression severity. While powerful, deep learning approaches lack interpretability. We developed an interpretable method of automatically measuring depression severity that uses barycentric coordinates of facial landmarks and a Lie-algebra based rotation matrix of 3D head motion.

Abstract Animations for the Communication and Assessment of Pain in Adults: Cross-Sectional Feasibility Study.

Background: Pain is the most common physical symptom requiring medical care, yet the current methods for assessing pain are sorely inadequate. Pain assessment tools can be either too simplistic or take too long to complete to be useful for point-of-care diagnosis and treatment.

Objective: The aim was to develop and test Painimation, a novel tool that uses graphic visualizations and animations instead of words or numeric scales to assess pain quality, intensity, and course.

Automatic Action Unit Detection in Infants Using Convolutional Neural Network.

Action unit detection in infants relative to adults presents unique challenges. Jaw contour is less distinct, facial texture is reduced, and rapid and unusual facial movements are common. To detect facial action units in spontaneous behavior of infants, we propose a multi-label Convolutional Neural Network (CNN).

Objective measurement of head movement differences in children with and without autism spectrum disorder.

Background: Deficits in motor movement in children with autism spectrum disorder (ASD) have typically been characterized qualitatively by human observers. Although clinicians have noted the importance of atypical head positioning (e.g.

Facial Expressiveness in Infants With and Without Craniofacial Microsomia: Preliminary Findings.

Objective: To compare facial expressiveness (FE) of infants with and without craniofacial macrosomia (cases and controls, respectively) and to compare phenotypic variation among cases in relation to FE.

Design: Positive and negative affect was elicited in response to standardized emotion inductions, video recorded, and manually coded from video using the Facial Action Coding System for Infants and Young Children.

Setting: Five craniofacial centers: Children's Hospital of Los Angeles, Children's Hospital of Philadelphia, Seattle Children's Hospital, University of Illinois-Chicago, and University of North Carolina-Chapel Hill.

Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding.

Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity.

Multimodal Detection of Depression in Clinical Interviews.

Current methods for depression assessment depend almost entirely on clinical interview or self-report ratings. Such measures lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of psychological disorder. We compared a clinical interview of depression severity with automatic measurement in 48 participants undergoing treatment for depression.

Head Movement Dynamics During Play and Perturbed Mother-Infant Interaction.

We investigated the dynamics of head movement in mothers and infants during an age-appropriate, well-validated emotion induction, the Still Face paradigm. In this paradigm, mothers and infants play normally for 2 minutes (Play) followed by 2 minutes in which the mothers remain unresponsive (Still Face), and then two minutes in which they resume normal behavior (Reunion). Participants were 42 ethnically diverse 4-month-old infants and their mothers.

Nonverbal Social Withdrawal in Depression: Evidence from manual and automatic analysis.

The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity.

Dyadic Behavior Analysis in Depression Severity Assessment Interviews.

Previous literature suggests that depression impacts vocal timing of both participants and clinical interviewers but is mixed with respect to acoustic features. To investigate further, 57 middle-aged adults (men and women) with Major Depression Disorder and their clinical interviewers (all women) were studied. Participants were interviewed for depression severity on up to four occasions over a 21 week period using the Hamilton Rating Scale for Depression (HRSD), which is a criterion measure for depression severity in clinical trials.

Interpersonal Coordination of Head Motion in Distressed Couples.

In automatic emotional expression analysis, head motion has been considered mostly a nuisance variable, something to control when extracting features for action unit or expression detection. As an initial step toward understanding the contribution of head motion to emotion communication, we investigated the interpersonal coordination of rigid head motion in intimate couples with a history of interpersonal violence. Episodes of conflict and non-conflict were elicited in dyadic interaction tasks and validated using linguistic criteria.

Automatic detection of pain intensity.

Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders.

Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions.

Humans recognize basic facial expressions effortlessly. Yet, despite a considerable amount of research, this task remains elusive for computer vision systems. Here, we compared the behavior of one of the best computer models of facial expression recognition (Z.