Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer\'s and Parkinson\'s. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and care.The code and data used in this work are available at: https://github.com/munsif200/Neurological-Health-Care.

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

UNASSIGNED: Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students.
UNASSIGNED: The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data.
UNASSIGNED: The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students\' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety.
UNASSIGNED: Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %).
UNASSIGNED: The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.

This study aims to explore AI-assisted emotion assessment in infants aged 6-11 months during complementary feeding using OpenFace to analyze the Actions Units (AUs) within the Facial Action Coding system. When infants (n = 98) were exposed to a diverse range of food groups; meat, cow-milk, vegetable, grain, and dessert products, favorite, and disliked food, then video recordings were analyzed for emotional responses to these food groups, including surprise, sadness, happiness, fear, anger, and disgust. Time-averaged filtering was performed for the intensity of AUs. Facial expression to different food groups were compared with neutral states by Wilcoxon Singed test. The majority of the food groups did not significantly differ from the neutral emotional state. Infants exhibited high disgust responses to meat and anger reactions to yogurt compared to neutral. Emotional responses also varied between breastfed and non-breastfed infants. Breastfed infants showed heightened negative emotions, including fear, anger, and disgust, when exposed to certain food groups while non-breastfed infants displayed lower surprise and sadness reactions to their favorite foods and desserts. Further longitudinal research is needed to gain a comprehensive understanding of infants\' emotional experiences and their associations with feeding behaviors and food acceptance.

OBJECTIVE: The present study was conducted to evaluate the use of computer-aided facial expression analysis to assess postoperative pain in children.
METHODS: This was a methodological observational study. The study population consisted of patients in the age group of 7-18 years who underwent surgery in the pediatric surgery clinic of a university hospital. The study sample consisted of 83 children who agreed to participate and met the sample selection criteria. Data were collected by the researcher using the Wong Baker Faces pain rating scale and Visual Analog Scale. Data were collected from the child, mother, nurse, and one external observer. Facial action units associated with pain were used for machine estimation. OpenFace was used to analyze the child\'s facial action units and Python was used for machine learning algorithms. The intraclass correlation coefficient was used for statistical analysis of the data.
RESULTS: The pain score predicted by the machine and the pain score assessments of the child, mother, nurse, and observer were compared. The pain assessment closest to the self-reported pain score by the child was in the order of machine prediction, mother, and nurse.
CONCLUSIONS: The machine learning method used in pain assessment in children performed well in estimating pain severity.It can code facial expressions of children\'s pain and reliably measure pain-related facial action units from video recordings.
CONCLUSIONS: The machine learning method for facial expression analysis assessed in this study can potentially be used as a scalable, standard, and valid pain assessment method for nurses in clinical practice.

Emotion recognition is a significant issue in many sectors that use human emotion reactions as communication for marketing, technological equipment, or human-robot interaction. The realistic facial behavior of social robots and artificial agents is still a challenge, limiting their emotional credibility in dyadic face-to-face situations with humans. One obstacle is the lack of appropriate training data on how humans typically interact in such settings. This article focused on collecting the facial behavior of 60 participants to create a new type of dyadic emotion reaction database. For this purpose, we propose a methodology that automatically captures the facial expressions of participants via webcam while they are engaged with other people (facial videos) in emotionally primed contexts. The data were then analyzed using three different Facial Expression Analysis (FEA) tools: iMotions, the Mini-Xception model, and the Py-Feat FEA toolkit. Although the emotion reactions were reported as genuine, the comparative analysis between the aforementioned models could not agree with a single emotion reaction prediction. Based on this result, a more-robust and -effective model for emotion reaction prediction is needed. The relevance of this work for human-computer interaction studies lies in its novel approach to developing adaptive behaviors for synthetic human-like beings (virtual or robotic), allowing them to simulate human facial interaction behavior in contextually varying dyadic situations with humans. This article should be useful for researchers using human emotion analysis while deciding on a suitable methodology to collect facial expression reactions in a dyadic setting.

Artificial intelligence (AI) has grown tremendously in the past decade. The application of AI in teledentistry can reform the way dental care, dental education, research, and subsequent innovations can happen remotely. Machine learning including deep learning-based algorithms can be developed to create predictive models of risk assessment for oral health related conditions, consequent complications, and patient stratification. Patients can be empowered to self-diagnose and apply preventive measures or self-manage some early stages of dental diseases. Applications of AI in teledentistry can be beneficial for both, the dental surgeon and the patient. AI enables better remote screening, diagnosis, record keeping, triaging, and monitoring of dental patients based on smart devices. This will take away rudimentary cases requiring run-of-the-mill treatments from dentists and enable them to concentrate on highly complex cases. This would also enable the dentists to serve a larger and deprived population in inaccessible areas. Its usage in teledentistry can bring a paradigm shift from curative to preventive personalised approach in dentistry. A strong asset to teledentistry could be a robust and comprehensive feedback mechanism routed through various channels proposed in this paper. This paper discusses the application of AI in teledentistry and proposes a feedback mechanism to enhance performance in teledentistry.

This article presents the analysis of the main Spanish political candidates for the elections to be held on April 2019. The analysis focuses on the Facial Expression Analysis (FEA), a technique widely used in neuromarketing research. It allows to identify the micro-expressions that are very brief, involuntary. They are signals of hidden emotions that cannot be controlled voluntarily. The video with the final interventions of every candidate has been post-processed using the classification algorithms given by the iMotions\'s AFFDEX platform. We have then analyzed these data. Firstly, we have identified and compare the basic emotions showed by each politician. Second, we have associated the basic emotions with specific moments of the candidate\'s speech, identifying the topics they address and relating them directly to the expressed emotion. Third, we have analyzed whether the differences shown by each candidate in every emotion are statistically significant. In this sense, we have applied the non-parametric chi-squared goodness-of-fit test. We have also considered the ANOVA analysis in order to test whether, on average, there are differences between the candidates. Finally, we have checked if there is consistency between the results provided by different surveys from the main media in Spain regarding the evaluation of the debate and those obtained in our empirical analysis. A predominance of negative emotions has been observed. Some inconsistencies were found between the emotion expressed in the facial expression and the verbal content of the message. Also, evidences got from statistical analysis confirm that the differences observed between the various candidates with respect to the basic emotions, on average, are statistically significant. In this sense, this article provides a methodological contribution to the analysis of the public figures\' communication, which could help politicians to improve the effectiveness of their messages identifying and evaluating the intensity of the expressed emotions.

The increase in digitalization, software applications, and computing power has widened the variety of tools with which to collect and analyze sensory data. As these changes continue to take place, examining new skills required among sensory professionals is needed. The aim with this study was to answer the following questions: (a) How did sensory professionals perceive the opportunities to utilize facial expression analysis in sensory evaluation work? (b) What skills did the sensory professionals describe they needed when utilizing facial expression analysis? Twenty-two sensory professionals from various food companies and universities were interviewed by using semistructural thematic interviews to map development intentions from facial expression recognition data as well as to describe the established skills that were needed. Participants\' facial expressions were first elicited by an odor sample during a sensory evaluation task. The evaluation was video recorded to characterize a facial expression software response (FaceReader™). The participants were interviewed regarding their opinions of the data analysis the software produced. The study findings demonstrate how using facial expression analysis contains personal and field-specific perspectives. Recognizability, associativity, reflectivity, reliability, and suitability were perceived as a personal perspective. From the field-specific perspective, professionals considered the received data valuable only if they had skills to interpret and utilize it. There is a need for an increase in training not only in IT, mathematics, statistics, and problem-solving, but also in skills related to self-management and ethical responsibility.

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