Tool For Recognition Of Emotions Research Articles

Enhancing emotion recognition using multimodal fusion of physiological, environmental, personal data

Human emotion recognition, crucial for interpersonal relations and human-building interaction, identifies emotions from various behavioral signals to improve user interactions. To enhance the performance of emotion recognition, this study proposed a novel model that fuses physiological, environmental, and personal data. A unique dataset was created via experiments conducted in an environmental chamber, and an emotion recognition model was subsequently developed using a multimodal fusion approach. The model transforms physiological data into 2D images to capture time series and spatial features and uniquely incorporates metadata, including environmental and personal data. The model’s generalizability was validated using a leave-one-sample-out approach. The result showed 31.6% reduction of error with a predicted area when physiological, environmental, and personal data were fused in the emotion recognition model, suggesting that incorporating various contextual factors beyond physiological changes, such as the surrounding environment and inherent or acquired individual traits, can significantly enhance the model’s understanding of emotions. Furthermore, the model was to be robust to individual differences, offering consistent emotion recognition across different subjects. These findings suggest that the proposed model can serve as a potent tool for emotion recognition in built environmental applications.

Development of a Framework for the Communication System Based on KNX for an Interactive Space for UX Evaluation

Domotics (Home Automation) aims to improve the quality of life of people by integrating intelligent systems within inhabitable spaces. While traditionally associated with smart home systems, these technologies have potential for User Experience (UX) research. By emulating environments to test products and services, and integrating non-invasive user monitoring tools for emotion recognition, an objective UX evaluation can be performed. To achieve this objective, a testing booth was built and instrumented with devices based on KNX, an international standard for home automation, to conduct experiments and ensure replicability. A framework was designed based on Python to synchronize KNX systems with emotion recognition tools; the synchronization of these data allows finding patterns during the interaction process. To evaluate this framework, an experiment was conducted in a simulated laundry room within the testing booth to analyze the emotional responses of participants while interacting with prototypes of new detergent bottles. Emotional responses were contrasted with traditional questionnaires to determine the viability of using non-invasive methods. Using emulated environments alongside non-invasive monitoring tools allowed an immersive experience for participants. These results indicated that the testing booth can be implemented for a robust UX evaluation methodology.

EEG-Based Multi-Class Emotion Recognition using Hybrid LSTM Approach

Emotion recognition is a crucial task in human-computer interaction, psychology, and neuroscience. Electroencephalogram (EEG)-based multi-class emotion recognition is a novel approach that aims to identify and classify human emotions by analysing EEG signals. Traditional methods of emotion recognition often face challenges in accurately identifying and classifying human emotions due to their complexity and subjectivity. EEG-based emotion recognition provides a direct and objective measure of three emotional states (positive, neutral, and negative), making it a promising tool for emotion recognition. The proposed hybrid LSTM approach combines the strengths of different traditional machine learning algorithms: Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The approach was tested on the EEG brainwave dataset, and LSTM achieved an accuracy of 95%, while the proposed hybrid LSTM-GNB, LSTM-SVM, LSTM-LR, and LSTM-DT models achieved 65%, 96%, 97%, and 96% accuracy, respectively. The contribution of this study is the development of a hybrid LSTM approach that combines the strengths of two different algorithms, resulting in higher accuracy for multi-class emotion recognition using EEG signals. The results demonstrate the potential of the hybrid LSTM approach for real-world applications such as emotion-based human-computer interaction and mental health diagnosis.

ANALYSIS AND EVALUATION OF BIOMETRIC AUTHENTICATION MEANS BASED ON THE IMAGE OF THE FACE AND IRIS OF THE STAFF OF CRITICAL INFRASTRUCTURE FACILITIES

Dedicated to the analysis and evaluation of biometric authentication systems for personnel of critical infrastructure facilities. It is shown that tools based on the image of the face and the iris of the eye have broad prospects, which is explained by the proven solutions in the field of face image analysis, the availability and distribution of video recording tools that allow the iris to be recorded simultaneously with the face with satisfactory quality. It was determined that one of the ways to improve the effectiveness of such tools is to increase the accuracy of face recognition and resistance to obstacles that cover part of the face. It is also shown that an additional direction of improving the effectiveness of biometrics can be recognition of the current psycho-emotional state of personnel of critical infrastructure facilities. The need to evaluate the effectiveness of face and emotion recognition tools based on face and iris images is determined. Based on the analysis of the literature, two groups of efficiency criteria were formed: basic and additional. Criteria characterizing the effectiveness of the recognition process are included in the main group, and criteria corresponding to the technical implementation features and service capabilities of recognition tools are included in the additional group. An evaluation of modern means of face and emotion recognition based on the image of the face and iris of the eye was carried out, and their non-compliance with a number of criteria was determined. It is proposed to correlate the ways of further research with the solution of the task of ensuring the fulfillment of the criteria related to the possibility of recognizing emotions and a person based on part of the image of the face, facial expressions and part of the image of the iris of the eye, with the technical implementation of expert solutions. The possibility of performing the specified task due to the use of modern neural network technologies is shown.

Emotional Intelligence in Humanoid Robots: Social and Technical Applications

Main challenge in the human–robot interaction is to endow the robots with emotional intelligence making the interaction more genuine and natural. Main aspect in achieving this goal is the robot’s capability to interpret human emotions. Destiny humanoid robot will be one of the most widely used agents in human-robot interaction. Destiny robot will be equipped with a module of facial expression recognition based on CNN (convolutional neural network) facial expression recognition model with high accuracy and capability. This module will be integrated into Destiny's SDK that will allow facial expression classification. Another approach is developing a tool for emotion recognition through vocal features, such as tone, timbre, pitch or loudness making it possible to acquire machine intelligence with emotional intelligence. This feature would be especially helpful for humans suffering from emotional disorders or from the inability to express emotions. The model can detect emotional signals in the human voice using CNN.

An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments

In the era of a workforce driven by automation and artificial intelligence, social and emotional skills are becoming increasingly relevant to online learning environments. Since social-emotional learning may be defined as a vital component of the learning process in professional instructional design practices, online learners not only need to develop the ability to apply their knowledge, attitudes, and skills but also to understand and manage their emotions. In which setting and achieving positive goals through social interaction, sharing feelings, and developing empathy for others can help with the process. This paper outlines the possibility of using emotion recognition, and social sharing of emotion techniques to support the regulation of emotion in pre-service teacher education. This study aimed to investigate pre-service teachers’ emotion recognition tools acquired by emotion tracker and physiological signals based on their perceptions (without a concrete experience and knowledge). Moreover, the predictive ability was examined along with the relationships between emotion recognition, social sharing of emotion, and emotion regulation. Finally, we investigated emotion adjustment techniques that can be adapted into mobile computer-supported collaborative learning (mCSCL). In this study, 183 pre-service teachers from three different teacher-education institutions in Thailand, were voluntarily participated based on convenience sampling. The results of a self-report via online survey revealed that most pre-service teachers own at least one of the mobile technologies e.g., smartphones, tablets, or laptops. However, there is an increasing number of additional gadgets and wearable devices like EarPods and smartwatches. At the current time, it is nearly impossible to use of the IoT and other wearable devices. According to their subjective impressions in which corresponded to emotion recognition in the scientific literature, Heart rate (HR) and Heart rate variability (HRV) have recognized the most possibilities for emotion detection among physiological signals. Regarding regression analysis, the two-predictor models of emotion recognition and the social sharing of emotion were also able to account for 31% of the variance in emotion regulation, p<.001, R2=.31, and 95% CI [.70, .77]. In addition, the mCSCL applications and the importance of these variables in different collaboration levels are also discussed.

Use of Emotion Recognition Tools in Psychiatry Said to Be Premature

Back to table of contents Previous article ViewpointsFull AccessUse of Emotion Recognition Tools in Psychiatry Said to Be PrematureJacob Lee, M.D.Jacob LeeSearch for more papers by this author, M.D.Published Online:28 Jan 2022https://doi.org/10.1176/appi.pn.2022.2.36AbstractThe extended COVID-19 pandemic has brought about untold changes across nearly every facet of life in the United States. As society looks to transition from the crisis phase toward a so-called “new normal,” many industries are making greater use of tools that enable productivity at a distance. In many cases, artificial intelligence (AI) has provided the foundation upon which these tools are developed. Emotion recognition tools (ERTs) are one example. These AI-powered technologies are alleged to be able to evaluate facial micro-expressions based on camera data, from which an emotional state is reported. Is your co-worker happy? Is this second grader angry? The potential benefits of these technologies are obvious, but the many pitfalls may not be.As this new field explodes onto the scene and is poised to reach a value of $20 billion to $60 billion by mid-decade, the stakes for an industry rightfully already under ethical scrutiny have been heightened. The central questions for these technologies regard how accurately software can identify facial expressions, and whether facial expressions are a reliable indicator of underlying emotion. The technology companies confidently assert that these questions are answered and have introduced their new technologies into diverse and ethically complex settings including private and national security, criminal justice, and health care. Already, software programs such as Woebot have introduced mental health–specific ERT applications to monitor depression, anxiety, and substance use. This program received FDA Breakthrough Device designation to monitor women for postpartum depression. While Woebot was designed and validated by Stanford-trained computer scientists and psychologists, many other programs have not been developed with such scientific rigor.Built upon models by Paul Ekman, Ph.D., to categorize facial movements into emotional taxonomies, current ERT technologies largely tout the ability to recognize “the six universal human emotions”: anger, disgust, fear, happiness, sadness, and surprise. Technologies such as the Enablex or Emotient software also claim to detect attention and arousal. Psychiatrists utilize facial expressions as part of a complex, multifactorial formulation of a patient, whereas these software programs rely primarily on graphical information captured by a webcam. For the same reasons Ekman’s ideas have been subject to increasing scrutiny by contemporary psychologists and psychiatrists, technologies built upon these models often fail to consider cultural differences and cultural factors.A landmark 2019 review in the journal Psychological Science in the Public Interest by Lisa Feldman Barrett, Ph.D., et al. found weak correlation between facial expressions and emotions. In short, while people do smile when happy, there are substantial differences across cultures, situations, and even individuals. Many female-presenting individuals are socialized to smile politely, which might not confer happiness or agreement. As these technologies are largely being developed by a small, homogenous group of programmers, particular caution is needed.Anticipating the dangers of this understudied technology, legislators within the European Union have already drafted an omnibus proposal to regulate AI in this and similar applications. The American think tank Brookings Institution called for a total ban of ERTs in law enforcement, citing civil liberty concerns.Just as medications and therapies are subject to intensive study prior to use on humans, psychiatrists should lobby for greater study of emotion analysis technologies prior to their further deployment. Child psychiatrists, in particular, should object to the use of these understudied technologies to analyze minors in ways that might have lasting negative consequences. Until these software programs are validated, we should approach their analyses with caution and skepticism. ■“Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements” is posted here.Jacob Lee, M.D., is a PGY-4 child/adolescent psychiatry fellow at the University of Hawaii and a member of the APA Committee on Climate Change and Mental Health. ISSUES NewArchived

Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents

Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents

Facial emotion recognition and its association with quality of life and socio-occupational functioning in patients with bipolar disorder and their first-degree relatives

Facial emotion recognition deficits (FERD) are common even in the remitted phase of bipolar disorder (BD). Research regarding FERD in first-degree relatives is inconclusive. This study aimed to assess the facial emotion recognition in remitted patients of bipolar disorder and first-degree relatives(FDR) in comparison with healthy controls. Correlation between FERD and quality of life and socio-occupational functioning was also assessed. It was an observational, cross-sectional study done at a tertiary hospital in India. Study population (n=75) included remitted patients of bipolar disorder (n=27), first-degree relatives of BD patients (FDR) (n=20) and healthy controls (HC) (n=28). Facial emotion recognition, social and occupational functioning, and quality of life (QoL) was measured using Tool for Recognition of Emotions in Neuropsychiatric Disorders, Social & Occupational Functioning Assessment Scale and World Health Organization Quality of Life-Bref, respectively, in all the participants. The BD group did significantly worse in facial emotion recognition in comparison to FDR and HC groups (p<0.001). Emotion recognition of fear, anger, surprise, and happy were most affected. FDR did not vary significantly from HC in facial emotion recognition. Lower scores on facial emotion recognition were associated with lower QoL in the social domain(p=0.006) and poorer socio- occupational functioning scores (p=0.01), but it was not significant within the BD group. FERD is seen in remitted patients of bipolar disorder but not in the first -degree relatives. FERD affects social quality of life and functioning. Poorer social functioning in remitted patients of bipolar disorder might be multifactorial and cannot be attributed solely to FERD.

FNIRS study of prefrontal activation during emotion recognition–A Potential endophenotype for bipolar I disorder?

BackgroundFacial emotion recognition (FER) deficit is documented in many psychiatric disorders, including bipolar disorder (BD). However, its role as a risk-marker in BD is not well researched. In the present study, we investigated the role of FER and the corresponding prefrontal neurohemodynamic changes (PNHC) with functional near infra-red spectroscopy (fNIRS) in patients with BD and subjects at high risk for BD compared to healthy subject. MethodsUsing a cross-sectional case-control design we compared 14 patients with first episode mania (FEM) in remission (BD group), 14 healthy siblings of BD patients (HR group), and 13 matched healthy subjects (HC group). FER was assessed using a computer-based task called Tool for Recognition of Emotions in Neuropsychiatric Disorders (TRENDS). Simultaneously, the corresponding PNHC was recorded with fNIRS. Kruskal Wallis H test was used to analyze between-group differences and Spearman's rho for correlation analysis. ResultsThe three groups were comparable on socio-demographics (all p>0.09) except education (p = 0.03). HR group had the most hyper-activation in the bilateral DLPFC during the TRENDS task (all p<0.05). There was no significant between-group differences in the FER performance and no significant correlation between the FER performance and the PNHC in the HR and BD groups (all p>0.35). LimitationsThe potential confounding effect of medications in the BD group. ConclusionsThe hyper-activation of the DLPCF in HR group during FER could indicate an increased risk for BD. However, the lack of similar findings in the BD group might reflect a possible normalizing effect of medications. It is equally likely that differences in the PNHC are detectable earlier than the differences in FER task performance during the course of the illness. This requires further exploration.

The emotion-facial expression link: evidence from human and automatic expression recognition.

While it has been taken for granted in the development of several automatic facial expression recognition tools, the question of the coherence between subjective feelings and facial expressions is still a subject of debate. On one hand, the "Basic Emotion View" conceives emotions as genetically hardwired and, therefore, being genuinely displayed through facial expressions. Consequently, emotion recognition is perceiver independent. On the other hand, the constructivist approach conceives emotions as socially constructed, the emotional meaning of a facial expression being inferred by the perceiver. Hence, emotion recognition is perceiver dependent. In order (1) to evaluate the coherence between the subjective feeling of emotions and their spontaneous facial displays, and (2) to compare the recognition of such displays by human perceivers and by an automatic facial expression classifier, 232 videos of expressers recruited to carry out an emotion elicitation task were annotated by 1383 human perceivers as well as by Affdex, an automatic classifier. Results show a weak consistency between self-reported emotional states by expressers and their facial emotional displays. They also show low accuracy both of human perceivers and of the automatic classifier to infer the subjective feeling from the spontaneous facial expressions displayed by expressers. However, the results are more in favor of a perceiver-dependent view. Based on these results, the hypothesis of genetically hardwired emotion genuinely displayed is difficult to support, whereas the idea of emotion and facial expression as being socially constructed appears to be more likely. Accordingly, automatic emotion recognition tools based on facial expressions should be questioned.

S68. DIFFERENTIAL PATTERN OF SOCIAL COGNITION IMPAIRMENT BETWEEN RURAL AND URBAN DWELLING PATIENTS OF SCHIZOPHRENIA AND ITS FUNCTIONAL CORRELATES

BackgroundCognitive deficits (both neuro & social Cognition) play a vital role in determining functional status in schizophrenia. It has been noted that functional outcomes are relatively better in Indian rural settings. This suggests that cognition might be better in rural patients. Considering the prevailing vast cultural differences there is paucity of research which delineates the differences in social cognition and its impact on the functional outcomes between rural and urban setups. Hence, we aim to explore differential impairment in social cognition in patients with schizophrenia residing in rural versus urban settings and their impact on real-world functioning.Methods122 patients diagnosed with either schizophrenia or schizoaffective disorder from the rural taluk of Thirthahalli and Turuvekere were compared with 97 patients with similar diagnosis visiting a teaching hospital with urban residence. All the 219 patients met the standardized criteria for remission from positive and disorganized symptoms and were compared on culturally validated tests of SC—Social Cognition Rating Tool in Indian Setting (SOCRATIS) & Tool for Recognition of Emotions in Neuropsychiatric Disorders (TRENDS) to assess theory of mind, social perception and emotion recognition and NC—(attention/vigilance, speed of processing, visual and verbal learning, working memory and executive functions). Groningen Social Disabilities Schedule (GSDS) was used for the assessment of social dysfunction of the patients. Based on past factor analytical studies on these tests, social cognition dimensions were grouped into inferential social cognition which comprised of 1st order theory of mind & 2nd order theory of mind Index & socio-emotional cognition which included faux pas recognition, emotional recognition & social perception indices. These were compared using analysis of covariance after controlling for neurocognitive composite performance and other confounders Correlation between social-cognition and functioning among the two groups was assessed using Pearson correlation.ResultsPatients from rural population had significantly better inferential social cognition whereas patients form urban population had significantly better socio-emotional cognition. ANCOVA showed that even after controlling for effects of age, gender, duration of illness, family history, number of hospitalization & neuro-cognition composite scores the differences were significant. Social cognition composite score was significantly (negatively) correlated with functional disability. The socio-emotional cognition component had a stronger association (proportion of variance explained) with functioning in both rural & urban samples (r= -0.411, r= -0.403 respectively). Inferential Social cognition from both rural & urban samples (r= -0.212, r= -0.238) also has significant association with functioning but of lesser magnitude as compared to the formerDiscussionThe two distinct components of social cognition - inferential and socio-emotional- were differentially impaired among rural & urban patients. With respect to its relationship with functioning, the socio-emotional cognition had a stronger association with functioning in both the groups. The reasons for the difference need to be explored by studying the socio-cultural characteristics of rural & urban dwelling patients which can moderate their expression of social cognition. These observations are critical in understanding how our micro- and macro-level environments can influence cognitive performance

Emotion Recognition, Emotion Awareness, Metacognition, and Social Functioning in Persons with Schizophrenia.

Background:Emotion processing has received significant research attention in persons with schizophrenia. However, some aspects of this construct, such as emotion awareness, are less researched. In addition, there is limited work on metacognitive awareness and social functioning in persons with schizophrenia.Methods:Our sample comprised of 27 participants with schizophrenia- and 26 nonclinical controls. The clinical group was assessed on Scale for Assessment of Positive Symptoms, Scale for Assessment of Negative Symptoms, Tool for Recognition of Emotions in Neuropsychiatric Disorders, Toronto Alexithymia Scale, Metacognitive Assessment Scale, self-reflectiveness subscale of Beck's Cognitive Insight Scale, Scale S and Scale U subscales of the Metacognitive Assessment Scale, and Groningen's Social Dysfunction Scale.Results and Conclusion:Participants with schizophrenia had greater deficits in emotion recognition than nonclinical controls (P = 0.05, df = 51). There was no significant correlation between emotion recognition and metacognition in the clinical group. The presence of negative symptoms was significantly associated with social functioning in persons with schizophrenia.Key messages:Clinical symptoms, in particular negative symptoms, play an important role in social functioning in persons with schizophrenia and it is necessary to address these along with social cognition in order to improve functioning.

Curative effect of progressively integrated sandplay therapy on core symptoms and sleep management in preschool children with mild-to-moderate autism spectrum disorder

To explore the curative effect of progressively integrated sandplay therapy on core symptoms and sleep management in preschool children with mild-to-moderate autism spectrum disorder (ASD). A total of 50 mild-to-moderate ASD preschool children were randomly divided into an experimental group (n=25) and a control group (n=25). The control group was treated with structured teaching and auditory integration training (AIT). Based on these, the experimental group was treated with progressively integrated sandplay therapy. The Autism Behavior Checklist (ABC), Children Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), Children's Sleep Habit Questionnaire (CSHQ) and emotion recognition tools were used to evaluate the overall curative effect before treatment and 3 months after treatment. The experimental group had significantly lower scores of feeling, social interaction, somatic motor, language factor, total ABC and total CARS than the control group (P<0.05). The experimental group had also significantly lower scores of social cognition, social communication, social motivation, autistic behavior factor and total SRS than the control group (P<0.05). The experimental group had a significantly higher accurate rate of recognition of the upper facial expression and lower facial expression than the control group (P<0.05). The experimental group had significantly lower scores of bedtime resistance, sleep onset, sleep duration, night waking, daytime sleepiness, and total CSHQ than the control group (P<0.05). The experimental group had significantly reduced themes of wounding and significantly increased themes of healing after sandplay therapy (P<0.05). Progressively integrated sandplay therapy can effectively improve the core symptoms and sleep quality of preschool children with mild-to-moderate ASD, which can be used as an early rehabilitation measure.

Parenting in mothers with schizophrenia and its relation to facial emotion recognition deficits- a case control study.

Difficulties in parenting and Facial Emotion Recognition deficits (FERD) in persons with schizophrenia have been demonstrated independently. However, the relationship between parenting deficits and FERD in mothers with schizophrenia is yet to be explored. The aims of the study were to assess parenting in mothers with schizophrenia, to examine the clinical correlates of parenting and to examine if facial emotion recognition deficits had an association with parenting. Fifty mothers with schizophrenia and 50 age matched healthy mothers were assessed for parenting and FERD. Parenting was assessed using Arnold's parenting scale and Parent Interview Schedule. Kiddie TRENDS, a modified Tool for Recognition of Emotions in Neuropsychiatric Disorders (TRENDS) was administered to assess facial emotion recognition. Among mothers with schizophrenia, FERD were noted in emotions of sadness, disgust, anger and surprise. Laxness in parenting was associated with negative symptoms of schizophrenia and also facial emotion recognition deficits. Parenting difficulties are commonly noticed in mothers with schizophrenia. Laxness style of parenting was found more often in mothers with schizophrenia, especially in mothers with blunt affect. Mothers with schizophrenia had difficulties in recognizing emotions of sadness, anger, disgust and surprise and this was associated with laxness style of parenting.

Development of a tool to assess Facial Emotion Recognition deficits in mothers with schizophrenia-A preliminary report.

There is dearth of studies which have assessed Facial Emotion Recognition Deficits (FERD) in parents and its impact on parenting. To assess FERD in mothers with severe mental illness, a modification of TRENDS (A Tool for Recognition of Emotions in Neuropsychiatric DisorderS) using images of children was developed. Two children emoted six basic emotions,namely happy, sad, fear, anger, surprise and disgust at two different intensities along with neutral facial expressions. These images were then validated by 20 subjects which included 15 healthy controls and 5 qualified psychiatrists.The tool was found to have good inter-rater agreement and internal consistency (Cronbach's alpha+0.712).

Schizophrenia patients experience substantial social cognition deficits across multiple domains in remission

Knowledge about SC (social cognition) during remission would inform us whether such deficits are trait- or state-markers of the disorder, as well as highlight its relevance for rehabilitation. We aimed to compare SC deficits and their relative independence from NC (neuro-cognition) deficits in remitted schizophrenia patients and matched health controls using comprehensive, culturally sensitive standardized tools. 60 schizophrenia patients meeting modified standardized criteria for remission and 60 age, gender and education matched healthy controls were compared on culturally validated tests of SC—Social Cognition Rating Tool in Indian Setting (SOCRATIS) & Tool for Recognition of Emotions in Neuropsychiatric Disorders (TRENDS) to assess theory of mind, attributional bias, social perception and emotion recognition and NC—(attention/vigilance, speed of processing, visual and verbal learning, working memory and executive functions). Patients had deficits in both SC and NC compared to healthy controls. Deficits in SC were largely independent of NC performance, and SC deficits persisted after adjusting for deficits in NC function. The effect sizes (Cohen's d) for SC deficits ranged from 0.37 to 2.23. All patients scored below a defined cut-off in at least one SC domain. SC deficits are likely to be state-independent in schizophrenia, as they are present in remission phase of the illness. This supports their status as a possible composite-endophenotype in schizophrenia.

Effect of yoga therapy on plasma oxytocin and facial emotion recognition deficits in patients of schizophrenia

Context:Yoga therapy has been demonstrated to be useful in treatment of negative symptoms and improving the socio-occupational functioning and emotion recognition deficits in antipsychotic-stabilized schizophrenia patients. Oxytocin has been recently implicated in social cognition deficits in schizophrenia. The effect of yoga therapy on oxytocin levels in schizophrenia has not been studied.Aims:This study aimed to assess the effect of yoga therapy on symptoms, socio-occupational functioning, facial emotion recognition deficits and plasma oxytocin levels in antipsychotic stabilized schizophrenia patients.Settings and Design:Randomized controlled study on 43 consenting, medication stabilized patients with schizophrenia in a tertiary psychiatric center using yoga intervention and waitlisted groups.Materials and Methods:A total of 43 schizophrenia patients were randomized to yoga group (n=15) or waitlist group (n=28). Patients in the yoga group received training in a specific yoga therapy module for schizophrenia. Patients in both groups were continued on stable antipsychotic medication. Assessments included scale for assessment of positive symptoms, scale for assessment of negative symptoms, socio-occupational functioning scale and tool for recognition of emotions in neuropsychiatric disorders (TRENDS) and plasma oxytocin levels; performed at baseline and at the end of 1 month.Results:A total of 15 patients in the yoga group and 12 in waitlist group completed the study. The yoga therapy group showed a significant improvement in socio-occupational functioning, performance on TRENDS (P<0.001) and plasma increase in oxytocin levels (P=0.01) as compared with the waitlist group.Conclusion:The study supported the role of add-on yoga therapy in management of schizophrenia and demonstrated an improvement in endogenous plasma oxytocin levels in schizophrenia patients receiving yoga therapy.

First rank symptoms &amp; facial emotion recognition deficits in antipsychotic naïve schizophrenia: Implications for social threat perception model

Facial emotion recognition deficits (FERD) have been consistently demonstrated in schizophrenia. However the relation between psychopathology and FERD remains inconclusive. This could possibly be due to the wide heterogeneity in the psychopathology of schizophrenia. First Rank Symptoms (FRS) of schizophrenia is associated with heightened sense of paranoia and rapid processing of threatful emotional stimuli. We studied differences in patterns of FERD between homogenous sub-groups of antipsychotic naïve schizophrenia patients (n=63); namely those experiencing FRS (FRS+ group n=26) and those who did not (FRS- group n=37), in comparison to age-, sex-, education matched healthy controls (n=45). FERD was assessed using TRENDS - (Tool for Recognition of Emotions in Neuropsychiatric DisorderS), a culturally sensitive and ecologically valid (consisting of both static and dynamic emotional stimuli) tool. The total number of images of non threatful emotions (sad, happy, neutral) which were identified as any of the threatful emotions (fear, anger, disgust) and vice versa were calculated and termed TRENDS Over-identification and Under-identification score respectively. The patient group made significantly greater errors in emotion recognition as compared to healthy controls. On post hoc analysis (Tukey HSD) the patients in FRS+ group made significantly greater errors in Over-identification as compared to the FRS- group. This study supports that FERD is one of the important deficits in schizophrenia. There is a differential pattern of impairment in FERD, which supports the role of heightened threat perception in the evolution of psychopathology in schizophrenia patients.

Effect of yoga therapy on facial emotion recognition deficits, symptoms and functioning in patients with schizophrenia

Facial emotion recognition deficits have been consistently demonstrated in schizophrenia and can impair socio-occupational functioning in these patients. Treatments to improve these deficits in antipsychotic-stabilized patients have not been well studied. Yoga therapy has been described to improve functioning in various domains in schizophrenia; however, its effect on FERD is not known. Antipsychotic-stabilized patients randomized to receive Yoga (n=27), Exercise (n=17) or Waitlist group (n=22) were assessed at baseline, 2nd month, and 4th month of follow-up by raters blind to group status. Assessments included Positive and Negative Syndrome Scale (PANSS), Socio-Occupational Functioning Scale (SOFS), and Tool for Recognition of Emotions in Neuropsychiatric DisorderS (TRENDS). There was a significant positive correlation between baseline FERD and socio-occupational functioning (r=0.3, P=0.01). Paired samples t test showed significant improvement in positive and negative symptoms, socio-occupational functioning and performance on TRENDS (P<0.05) in the Yoga group, but not in the other two groups. Maximum improvement occurred at the end of 2 months, and improvement in positive and negative symptoms persisted at the end of 4 months. Yoga therapy can be a useful add-on treatment to improve psychopathology, FERD, and socio-occupational functioning in antipsychotic-stabilized patients with schizophrenia.