Search Results (1,051)

The T-Rehab System delivers a semi-immersive tele-rehabilitation experience by integrating Affective Computing (AC) through facial expression analysis and contactless heartbeat monitoring. T-Rehab closely monitors patients’ mental health as they engage in a personalized, semi-immersive Virtual Reality (VR) game on a desktop PC, using a webcam with MediaPipe to track their hand movements for interactive exercises, allowing the system to tailor treatment content for increased engagement and comfort. T-Rehab’s evaluation comprises two assessments: system performance and cognitive walkthroughs. The first evaluation focuses on system performance, assessing the tested game, middleware, and facial emotion monitoring to ensure hardware compatibility and effective support for AC, gaming, and tele-rehabilitation. The second evaluation uses cognitive walkthroughs to examine usability, identifying potential issues in emotion detection and tele-rehabilitation. Together, these evaluations provide insights into T-Rehab’s functionality, usability, and impact in supporting both physical rehabilitation and emotional well-being. The thorough integration of technology inside T-Rehab ensures a holistic approach to tele-rehabilitation, allowing patients to participate comfortably and efficiently from anywhere. This technique not only improves physical therapy outcomes but also promotes mental resilience, marking an important step advance in tele-rehabilitation practices. Full article

Background/Objectives: Crisponi/cold-induced sweating syndrome 1 (CISS1/CISS, MIM#272430) is a genetic disorder due to biallelic variants in CRFL1 (MIM*604237). The related phenotype is mainly characterized by abnormal thermoregulation and sweating, facial muscle contractions in response to tactile and crying-inducing stimuli at an early age, skeletal anomalies (camptodactyly of the hands, scoliosis), and craniofacial dysmorphisms, comprising full cheeks, micrognathia, high and narrow palate, low-set ears, and a depressed nasal bridge. The condition is associated with high lethality during the neonatal period and can benefit from timely symptomatic therapy. Methods: We collected frontal images of all patients with CISS1/CISS published to date, which were analyzed with Face2Gene (F2G), a machine-learning technology for the facial diagnosis of syndromic phenotypes. In total, 75 portraits were subdivided into three cohorts, based on age (Cohort 1 and 2) and the presence of the typical facial trismus (Cohort 3). These portraits were uploaded to F2G to test their suitability for facial analysis and to verify the capacity of the AI tool to correctly recognize the syndrome based on the facial features only. The photos which passed this phase (62 images) were fed to three different AI algorithms—DeepGestalt, Facial D-Score, and GestaltMatcher. Results: The DeepGestalt algorithm results, including the correct diagnosis using a frontal portrait, suggested a similar facial phenotype in the first two cohorts. Cohort 3 seemed to be highly differentiable. The results were expressed in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and p Value. The Facial D-Score values indicated the presence of a consistent degree of dysmorphic signs in the three cohorts, which was also confirmed by the GestaltMatcher algorithm. Interestingly, the latter allowed us to identify overlapping genetic disorders. Conclusions: This is the first AI-powered image analysis in defining the craniofacial contour of CISS1/CISS and in determining the feasibility of training the tool used in its clinical recognition. The obtained results showed that the use of F2G can reveal valid support in the diagnostic process of CISS1/CISS, especially in more severe phenotypes, manifesting with facial contractions and potentially lethal consequences. Full article

Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs. Results: Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems. Discussion: These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human–computer interaction, and marketing. Conclusions: This review provides critical insight into and suggestions for further development in the field of EEG-based emotion recognition toward more robust, scalable, and ethical applications by consolidating current methodologies and identifying their key limitations. Full article

This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evaluate human pain intensity levels. This integration aims to enhance the recognition system’s performance and enable a more accurate assessment of pain intensity. Such a multimodal approach supports improved decision making in real-time patient care, addressing limitations inherent in unimodal systems for measuring pain sentiment. So, the primary contribution of this work lies in developing a multimodal pain sentiment analysis system that integrates the outcomes of image-based and audio-based pain sentiment analysis models. The system implementation contains five key phases. The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. In the second phase, the system extracts discriminant and divergent features from the facial region using deep learning techniques, utilizing some convolutional neural network (CNN) architectures, which are further refined through transfer learning and fine-tuning of parameters, alongside fusion techniques aimed at optimizing the model’s performance. The third phase performs the speech-audio recording preprocessing; the extraction of significant features is then performed through conventional methods followed by using the deep learning model to generate divergent features to recognize audio-based pain sentiments in the fourth phase. The final phase combines the outcomes from both image-based and audio-based pain sentiment analysis systems, improving the overall performance of the multimodal system. This fusion enables the system to accurately predict pain levels, including ‘high pain’, ‘mild pain’, and ‘no pain’. The performance of the proposed system is tested with the three image-based databases such as a 2D Face Set Database with Pain Expression, the UNBC-McMaster database (based on shoulder pain), and the BioVid database (based on heat pain), along with the VIVAE database for the audio-based dataset. Extensive experiments were performed using these datasets. Finally, the proposed system achieved accuracies of 76.23%, 84.27%, and 38.04% for two, three, and five pain classes, respectively, on the 2D Face Set Database with Pain Expression, UNBC, and BioVid datasets. The VIVAE audio-based system recorded a peak performance of 97.56% and 98.32% accuracy for varying training–testing protocols. These performances were compared with some state-of-the-art methods that show the superiority of the proposed system. By combining the outputs of both deep learning frameworks on image and audio datasets, the proposed multimodal pain sentiment analysis system achieves accuracies of 99.31% for the two-class, 99.54% for the three-class, and 87.41% for the five-class pain problems. Full article

A significant challenge that hinders advancements in medical research is the sensitive and confidential nature of patient data in available datasets. In particular, sharing patients’ facial images poses considerable privacy risks, especially with the rise of generative artificial intelligence (AI), which could misuse such data if accessed by unauthorized parties. However, facial expressions are a valuable source of information for doctors and researchers, which creates a need for methods to derive them without compromising patient privacy or safety by exposing identifiable facial images. To address this, we present a quick, computationally efficient method for detecting action units (AUs) and their intensities—key indicators of health and emotion—using only 3D facial landmarks. Our proposed framework extracts 3D face landmarks from video recordings and employs a lightweight neural network (NN) to identify AUs and estimate AU intensities based on these landmarks. Our proposed method reaches a 79.25% F1-score in AU detection for the main AUs, and 0.66 in AU intensity estimation Root Mean Square Error (RMSE). This performance shows that it is possible for researchers to share 3D landmarks, which are far less intrusive, instead of facial images while maintaining high accuracy in AU detection. Moreover, to showcase the usefulness of our AU detection model, using the detected AUs and estimated intensities, we trained state-of-the-art Deep Learning (DL) models to detect pain. Our method reaches 91.16% accuracy in pain detection, which is not far behind the 93.14% accuracy obtained when employing a convolutional neural network (CNN) with residual blocks trained on actual images and the 92.11% accuracy obtained when employing all the ground-truth AUs. Full article

Individual differences in attachment orientations may influence how we process emotionally significant stimuli. As one of the most important sources of emotional information are facial expressions, we examined whether there is an association between adult attachment styles (i.e., scores on the ECR questionnaire, which measures the avoidance and anxiety dimensions of attachment), facial emotion perception and face memory in a neurotypical sample. Trait and state anxiety were also measured as covariates. Eye-tracking was used during the emotion decision task (happy vs. sad faces) and the subsequent facial recognition task; the length of fixations to different face regions was measured as the dependent variable. Linear mixed models suggested that differences during emotion perception may result from longer fixations in individuals with insecure (anxious or avoidant) attachment orientations. This effect was also influenced by individual state and trait anxiety measures. Eye movements during the recognition memory task, however, were not related to either of the attachment dimensions; only trait anxiety had a significant effect on the length of fixations in this condition. The results of our research may contribute to a more accurate understanding of facial emotion perception in the light of attachment styles, and their interaction with anxiety characteristics. Full article

Supraorbital hollowness and pretarsal fullness, commonly known as the sunken eyelid and charming roll, respectively, are significant anatomical features that impact the aesthetic appearance of the periorbital region. Supraorbital hollowness is characterized by a recessed appearance of the upper eyelid, often attributed to genetic factors, aging, or surgical alterations, such as excessive fat removal during blepharoplasty. This condition is particularly prevalent among East Asians due to anatomical differences, such as weaker levator muscles and unique fat distribution patterns. Pretarsal fullness, also known as aegyo-sal, enhances the youthful and expressive appearance of the lower eyelid, forming a roll above the lash line that is considered aesthetically desirable in East Asian culture. Anatomical-based filler injection techniques are critical for correcting these features, involving precise placement within the correct tissue planes to avoid complications and achieve natural-looking results. This approach not only improves the aesthetic appeal of the eyelid but also enhances the overall facial harmony, emphasizing the importance of tailored procedures based on individual anatomy and cultural preferences. Full article

Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expressions, no AI models have been developed to estimate these affective states from facial images based on empirical data. We developed a recurrent neural network-based AI model to estimate subjective valence and arousal states from facial images. We trained our model using a database containing participant valence/arousal states and facial images. Leave-one-out cross-validation supported the validity of the model for predicting subjective valence and arousal states. We further validated the effectiveness of the model by analyzing a dataset containing participant valence/arousal ratings and facial videos. The model predicted second-by-second valence and arousal states, with prediction performance comparable to that of FaceReader, a commercial AI model that estimates dimensional affective states based on a different approach. We constructed a graphical user interface to show real-time affective valence and arousal states by analyzing facial video data. Our model is the first distributable AI model for sensing affective valence and arousal from facial images/videos to be developed based on an empirical database; we anticipate that it will have many practical uses, such as in mental health monitoring and marketing research. Full article

This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child–robot interaction (CRI). By leveraging interdisciplinary insights from neuroscience, psychology, and artificial intelligence, the study focuses on creating adaptive, emotion-aware systems capable of dynamically recognizing and responding to human emotional states. Through a real-world CRI pilot study involving the NAO robot, this research demonstrates how facial expression analysis and speech emotion recognition can be employed to detect and address negative emotions in real time, fostering positive emotional engagement. The emotion recognition system combines handcrafted and deep learning features for facial expressions, achieving an 85% classification accuracy during real-time CRI, while speech emotions are analyzed using acoustic features processed through machine learning models with an 83% accuracy rate. Offline evaluation of the combined emotion dataset using a Dynamic Bayesian Mixture Model (DBMM) achieved a 92% accuracy for facial expressions, and the multilingual speech dataset yielded 98% accuracy for speech emotions using the DBMM ensemble. Observations from psychological and technological aspects, coupled with statistical analysis, reveal the robot’s ability to transition negative emotions into neutral or positive states in most cases, contributing to emotional regulation in children. This work underscores the potential of emotion-aware robots to support therapeutic and educational interventions, particularly for pediatric populations, while setting a foundation for developing personalized and empathetic human–machine interactions. These findings demonstrate the transformative role of affective computing in bridging the gap between technological functionality and emotional intelligence across diverse domains. Full article

This study aimed to investigate the roles of facial information processing and math anxiety in estimation performance. Across three experiments, participants completed a two-digit multiplication estimation task under the conditions of emotion judgment (Experiment 1), identity judgment (Experiment 2), and combined emotion and identity judgment (Experiment 3). In the estimation task, participants used either the down-up or up-down problem to select approximate answers. In Experiment 1, we found that negative emotions impair estimation performance, while positive and consistent emotions have a facilitating effect on estimation efficiency. In Experiment 2, we found that emotion and identity consistency interact with each other, and negative emotions actually promote estimation efficiency when identity is consistent. In Experiment 3, we found that emotion, identity consistency, and emotional consistency have complex interactions on estimation performance. Moreover, in most face-processing conditions, participants’ estimation performance is not affected by their level of math anxiety. However, in a small number of cases, mean proportions under happy and fearful conditions are negatively correlated with math anxiety. Full article

This study aimed to explore the influence of various mask attributes on the recognition of micro-expressions (happy, neutral, and fear) and facial favorability under different background emotional conditions (happy, neutral, and fear). The participants were asked to complete an ME (micro-expression) recognition task, and the corresponding accuracy (ACC), reaction time (RT), and facial favorability were analyzed. Results: (1) Background emotions significantly impacted the RT and ACC in micro-expression recognition, with fear backgrounds hindering performance. (2) Mask wearing, particularly opaque ones, prolonged the RT but had little effect on the ACC. Transparent masks and non-patterned masks increased facial favorability. (3) There was a significant interaction between background emotions and mask attributes; negative backgrounds amplified the negative effects of masks on recognition speed and favorability, while positive backgrounds mitigated these effects. This study provides insights into how masks influence micro-expression recognition, crucial for future research in this area. Full article

Background/Objectives: Fake pain expressions are more intense, prolonged, and include non-pain-related actions compared to genuine ones. Despite these differences, individuals struggle to detect deception in direct tasks (i.e., when asked to detect liars). Regarding neural correlates, while pain observation has been extensively studied, little is known about the neural distinctions between processing genuine, fake, and suppressed pain facial expressions. This study seeks to address this gap using authentic pain stimuli and an implicit emotional processing task. Methods: Twenty-four healthy women underwent an fMRI study, during which they were instructed to complete an implicit gender discrimination task. Stimuli were video clips showing genuine, fake, suppressed pain, and neutral facial expressions. After the scanning session, participants reviewed the stimuli and rated them indirectly according to the intensity of the facial expression (IE) and the intensity of the pain (IP). Results: Mean scores of IE and IP were significantly different for each category. A greater BOLD response for the observation of genuine pain compared to fake pain was observed in the pregenual anterior cingulate cortex (pACC). A parametric analysis showed a correlation between brain activity in the mid-cingulate cortex (aMCC) and the IP ratings. Conclusions: Higher IP ratings for genuine pain expressions and higher IE ratings for fake ones suggest that participants were indirectly able to recognize authenticity in facial expressions. At the neural level, pACC and aMCC appear to be involved in unveiling the genuine vs. fake pain and in coding the intensity of the perceived pain, respectively. Full article

This study examined the impact of males’ facial and vocal attractiveness, as well as social interest in females’ decision-making in a trust game. The results showed that trustees with attractive faces or expressing positive social interest were more likely to receive initial investments. Trustees with attractive voices also received more initial investments than unattractive ones in most conditions, except when they had attractive faces and positive interest. Moreover, participants reinvest in trustees with attractive faces or voices, even if they withheld repayment. However, trustees with positive interest would receive more reinvestment only when they reciprocated. In addition, trusters expressing positive social interest were expected to invest and earn repayment at higher rates. Nonetheless, trusters with attractive faces (or voices) were only expected to invest at higher rates when they had attractive voices (or faces) and negative interest. These findings suggest that beauty premium is modulated by participants’ roles, such that the effect of beauty would be stronger when participants encounter trustees rather than trusters. Positive social interest is a perk in most conditions, except when trustees withheld repayment. Full article

Background: The characterization of facial sexual dimorphic patterns in healthy populations serves as valuable normative data to tailor functionally effective surgical treatments and predict their aesthetic outcomes and to identify dysmorphic facial traits related to hormonal disorders and genetic syndromes. Although the analysis of facial sexual differences in juveniles of different ages has already been investigated, few studies have approached this topic with three-dimensional (3D) geometric morphometric (GMM) analysis, whose interpretation may add important clinical insight to the current understanding. This study aims to investigate the location and extent of facial sexual variations in juveniles through a spatially dense GMM analysis. Methods: We investigated 3D stereophotogrammetric facial scans of 304 healthy Italians aged 3 to 18 years old (149 males, 155 females) and categorized into four different age groups: early childhood (3–6 years), late childhood (7–12 years), puberty (13–15 years), and adolescence (16–18 years). Geometric morphometric analyses of facial shape (allometry, general Procrustes analysis, Principal Component Analysis, Procrustes distance, and Partial Least Square Regression) were conducted to detail sexually dimorphic traits in each age group. Results: The findings confirmed that males have larger faces than females of the same age, and significant differences in facial shape between the two sexes exist in all age groups. Juveniles start to express sexual dimorphism from 3 years, even though biological sex becomes a predictor of facial soft tissue morphology from the 7th year of life, with males displaying more protrusive medial facial features and females showing more outwardly placed cheeks and eyes. Conclusions: We provided a detailed characterization of facial change trajectories in the two sexes along four age classes, and the provided data can be valuable for several clinical disciplines dealing with the craniofacial region. Our results may serve as comparative data in the early diagnosis of craniofacial abnormalities and alterations, as a reference in the planning of personalized surgical and orthodontic treatments and their outcomes evaluation, as well as in several forensic applications such as the prediction of the face of missing juveniles. Full article

The quest for surgical advancements regarding the enhancement of the submental and cervicofacial regions has witnessed a remarkable upsurge in recent years. Informed patients are actively seeking sophisticated plastic surgery techniques to achieve comprehensive rejuvenation in these specific areas. Common complaints expressed by these patients include sagging of the jawline, the emergence of deep perioral wrinkles, and the formation of “marionette lines” within the lower third of the face. Furthermore, the manifestation of age-related signs, including neck laxity, submental adipose accumulation, “witch’s chin” deformity, and weakened platysma musculature, are common within this anatomical region. This literature review aims to summarize recent technical improvements, historical evolution, indications, postoperative care, and challenges for facial rejuvenation of the lower third of the face and neck. The application of minimally invasive procedures as part of a comprehensive approach for an aging face will also be discussed. In this article, an extensive search of the available literature was conducted using leading databases, including PubMed and MEDLINE, with the keywords “neck lift”, “platysmaplasty”, “facial rejuvenation”, “medial platysmaplasty”, “lateral platysmaplasty”, “neck rejuvenation”, and “cervicofacial rejuvenation”. Full article