An Early Warning Method of College Students’ Psychological Crisis Based on Emotion Recognition

Early warning and intervention of college students' psychological crisis are becoming an important subject to college mental health education. However, there is a lack of relevance and effectiveness in warning and intervention of college students' psychological crisis. With the development of big data mining techniques, the application of using big data technology in the early warning and intervention of college students' psychological crisis will improve the accuracy of college students' psychological crisis detection and establish an effective model of crisis early warning in order to make risk intervention in time. This paper discusses the application of big data in the field of the early warning and intervention of college students 'psychological crisis and establishes an effective countermeasure of early warning and intervention of psychological crisis and builds the mental health education team so as to dynamically grasp the condition of their psychological health to effectively develop mental health education.

In recent years, college students' psychological problems have occurred frequently, and the early warning of college students' psychological crisis has received social attention. Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. Applying artificial intelligence and big data to the practice of college students' mental health education plays a very positive role in accurately finding and scientifically solving college students' mental health problems. This paper combs the current application and research of artificial intelligence and big data in college students' mental health education and then clarifies the problems existing in the practical application. Finally, on the basis of in-depth analysis of the characteristics of college students' psychological crisis, the paper designs college students' psychological crisis early warning data collection system from six aspects, including the educational administration system and the access control system. And from the aspects of establishing a multilevel linkage feedback early warning system, building a team of big data technical personnel and mental health education personnel, it puts forward countermeasures for college students' psychological crisis, so as to provide theoretical and methodological support for college mental health management.

The main aim of this thesis was to evaluate the efficacy of an emotion training intervention for young children with autism. Further aims were to investigate the relationship between emotion recognition ability, autism symptom severity and social skills in young children with autism. The first chapter of this thesis provides a review of the literature on emotion recognition skills of children with autism. It has been suggested that difficulties in recognising and responding to emotions may underlie the social deficits that are a core feature of autism (Baron-Cohen, Golan, & Ashwin, 2009). However research varies regarding the exact nature of emotion recognition skills of children with autism. Review of the literature suggests that children with autism and comorbid ID have some difficulty matching and labelling basic facial expressions of emotion, for both human and non-human faces, across static and dynamic conditions, when compared with typically developing children and those with other clinical disorders matched for mental age, VIQ or PIQ. For children with high-functioning Autism Spectrum Disorders (ASD), results vary due to large differences between studies. In general, research suggests that children with high-functioning autism have emotion recognition difficulties relative to typically developing children matched for chronological age and cognitive ability, and that these difficulties may be specific to the recognition of complex, but not basic, emotional expressions. These findings highlight the importance of emotion training programmes for young children with autism. Chapter 1 also discusses research in to the relationship between emotion recognition ability and social skills for individuals with autism. Some research has reported a specific association between reduced accuracy in the recognition of expressions of sadness and reduced social skills for adolescents and adults with ASD (Boraston, Blakemore, Chilvers & Skuse, 2007; Wallace et al., 2011) but no research has investigated this association for children with autism. It was suggested that future research might benefit from the investigation of the relationship between emotion recognition skills and social skills in young children with autism. Chapter 2 of this thesis provides a review of research evaluating emotion recognition interventions for children with autism, with the aim of identifying areas of research requiring further investigation. Review of the literature suggested that most emotion training programmes designed for children with autism have been evaluated with older, high-functioning children. Few of these programmes have been evaluated with young children with autism and none have been trialled with young children with autism and comorbid intellectual disability. One of the intervention programmes reviewed in Chapter 2 (the Transporters; Changing Media Development, 2006) was designed for use with young children with autism but had only been evaluated with high-functioning children with ASD. Further research was needed to evaluate its efficacy with young children with autism with a lower range of cognitive ability. The main focus of the current study was to investigate the efficacy of an emotion training programme (the Transporters) for use with a group of 55 young children with autism of a lower range of intellectual ability. To address limitations in previous research the current study also investigated the relationship between emotion recognition ability, autism symptom severity and social skills for young children with autism, with analyses based on the intervention study’s baseline assessment data. Three research papers were developed from this data and are presented in Chapters 4 to 6 as submitted journal articles. Paper 1 was a randomised controlled trial evaluating the efficacy of the Transporters emotion recognition training programme for use with a group of 55 young children with autism, aged four to seven years (FSIQ 42-107). Previous research evaluating the use of the Transporters programme suggested that it was effective in teaching emotion recognition skills to high-functioning children with ASD (e.g., Golan et al., 2010). However in the current study the Transporters programme showed limited efficacy in teaching basic emotion recognition skills to young children with autism of a lower range of cognitive ability. Improvements were limited to the recognition of expressions of anger, with poor maintenance of these skills at follow-up, and with no generalisation of skills to Theory of Mind or social skills. These findings provide limited support for the efficacy of the Transporters programme for young children with autism of a lower cognitive ability, with results suggesting that the Transporters may be more efficacious for older, higher-functioning children with autism. Paper 2 assessed the relationship between degree of autism symptom severity and emotion recognition ability for 55 young children with autism. Only two previous studies had been published on this topic, with findings suggesting that increased autism symptom severity was related to reduced accuracy in emotion recognition in older children and adolescents with ASD. More research was needed to investigate the relationship between autism symptom severity and emotion recognition skills in young children with autism. In support of previous findings, the current study showed that higher autism severity scores were associated with reduced accuracy in the recognition of facial expressions of fear and anger, as well as decreased accuracy in the identification of desire-based but not situation-based emotions. These findings suggest that emotion recognition difficulties may be more pronounced for children with more severe levels of autism symtomatology. Paper 3 investigated the relationship between emotion recognition ability and social skills for a sub-group of 42 young children with autism. Three previous studies had been published on this topic, but further research was needed into the association between accuracy in the recognition of specific emotions (happiness, sadness, anger and fear) and social skills in young children with autism. In the current study, analyses indicated that accuracy in the recognition of expressions of sadness (but not happiness, anger or fear) was associated with better social skills. These findings extend previous research with adults with ASD, suggesting that accuracy in the recognition of sadness is also related to better social skills in children with autism. The discussion chapter comments on the limitations of the current study, as well as the clinical implications and future research directions generated by this research. The main limitation of the current study was that outcome measures were limited the use of four basic facial expressions of emotion. This was due to the young developmental age of the participants, the cognitive demands of the emotion recognition tasks, and difficulty obtaining standardised measures of emotion recognition ability that could be reliably used with children with autism of a young developmental age. As a result, it is unknown whether children improved in their recognition of the more complex emotions that were also targeted in the Transporters programme. Further research is needed to develop and evaluate emotion recognition stimuli for use with young children with autism of a young developmental age. These materials would assist in the evaluation of emotion training programmes for young children with autism. The main conclusion of the current study was that the Transporters emotion training programme showed limited efficacy for use with young children with autism of a lower range of cognitive ability. The Transporters programme may instead be more efficacious for older, higher-functioning, children with autism. Collectively the current findings suggest that there is a need to identify more effective interventions to help teach emotion recognition skills to children with autism of a young developmental age, including those with comorbid ID.

Aimed at the causes and specific manifestations of college students’ psychological crisis, an early warning and intervention mechanism combined with the theory of social support was proposed for college students’ psychological crisis. Particularly, from the perspectives of school partners, teachers, relatives and friends, and schools, we proposed support measures for college students in psychological crisis, so as to create a healthy and active learning and living environment, in order to promote college students’ mental health, and to cultivate a qualified builder with a comprehensive combination of morality, intelligence, physical education, art and labor.

Thinking versus feeling: How interoception and cognition influence emotion recognition in behavioural-variant frontotemporal dementia, Alzheimer's disease, and Parkinson's disease

Expression recognition or Emotional state recognition using holistic and feature information is the vital step in Driver Assistance System. Many researchers have work on Facial Gesture or Emotion recognition independently. The purpose of the present paper is to deal with Simultaneous Facial Gesture tracking and Emotion recognition with Soft Computing tool like Fuzzy rule based system (FBS). In Human Centered Transportation large number of road accidents took place due to drowsiness or bad mood of the driver. The system proposed in this paper take into account both the Facial Gesture tracking and Emotion recognition so that if there is any sign of less attentiveness of the driver or driver's fatigue the car will be switch to automatic mode. A novel fuzzy system is created, whose rules is being defined through analysis of Facial Gesture variations. The idea behind this paper is to detect Facial Gesture by detecting the motion of eyes & lips along with classification of different facial expressions into one of the four basic human emotions, viz. happy, anger, sad, and surprise with fuzzy rule based system for better system performance. The given system proposes 91.66% accuracy for Facial Gesture detection & 90% accuracy for Emotion recognition while using Simultaneous Facial Gesture detection and Emotion recognition it provides 94.58% accuracy.

Sleep Deprivation and Emotion Recognition

Perceived discrimination constitutes an essential factor influencing the psychological crisis of college students. While prior research has examined the impact of discrimination on mental health in China, this study is the first to systematically investigate the chain mediating role of sense of life meaning and self-esteem in the relationship between perceived discrimination and psychological crisis. This approach not only enhances the theoretical framework of the relationship between discrimination and mental health but also offers a novel perspective for understanding discrimination-induced psychological crises in Chinese society. The present study used a questionnaire survey to test whether perceived discrimination may be associated with psychological crisis in Chinese college students. The mediating roles of sense of life meaning and self-esteem were also examined. In total, 514 college students were recruited to complete four scales, including the Perceived Discrimination Questionnaire, the Sense of Life Meaning Questionnaire, the Self-Esteem Scale, and the college student psychological crisis screening scale. The findings are: (1) Perceived discrimination, sense of life meaning, and self-esteem have significant direct predictive effects on college students' psychological crisis; and (2) sense of life meaning and self-esteem play a chain-mediating role in the relationship between perceived discrimination and psychological crisis of college students. The mediating effect includes two paths: perceived discrimination → self-esteem → psychological crisis (effect size: 0.130) and perceived discrimination → sense of life meaning → self-esteem → psychological crisis (effect size: 0.030). This research highlights that perceived discrimination can directly predict the psychological crisis of Chinese college students, and it can indirectly influence the level of psychological crisis of Chinese college students through the chain-mediating effect of sense of life meaning and self-esteem. The findings provide colleges and universities with valuable insights into the causes of students' psychological crises, aiding in the adjustment of mental health education strategies and formulation of effective support systems for discriminated students. Additionally, this study offers a robust scientific foundation for policymakers to develop and promote anti-discrimination policies, and foster social harmony.

With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students' emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.

The early warning of College Students' psychological crisis is the key link of prevention, and the early warning index system is the core of the early warning work. In recent years, scholars have made more and more achievements in the research of College Students' psychological crisis early warning index system, including the theoretical basis, content and construction methods of the early warning index system. There are six problems in the relevant research, such as the lack of theoretical research on the selection of indicator system, the unclear classification standard, the single construction method, the lack of weight distribution research, the lack of reliability and validity test on the selection results, and the lack of specific application research on indicators in practical work.

This paper presents an in-depth analysis and research on the identification of psychological crisis signals of college students using the optimized Dufferin equation. The early warning index system of college students’ psychological crisis was established and tested on 300 junior college students, and the early warning system of college students’ psychological crisis was established by using structural equation model, focusing on the mediating effect of coping mode between stress source and stress response and the mediating effect of stress source between social support and stress response. At the same time, the characteristics of psychological crises among college students of different genders and grades were compared and analyzed. To address the shortcomings of the classical Dufferin equation with limited noise immunity, the use of a higher-order double-coupled Dufferin system was further improved. A detection model based on the higher-order double-coupled system was established, and its feasibility was verified by the psychological crisis signal. The geometric features of the phase trajectory are adopted as the basis for judging the system state, which greatly reduces the computational effort. Based on defining the conceptual connotation of college students’ psychological crisis behavior system, the vulnerability of college students’ psychological crisis behavior system is interpreted from the perspective of system self-organization theory, and the vulnerability of college students’ psychological crisis behavior is mainly expressed in latent and manifest states, and its vulnerability transformation is a self-organization process. A questionnaire survey was conducted for ordinary college students to examine the performance of college students’ vulnerability state of the subject who endured college students’ psychological crisis behavior, and it was concluded that most college students appear to be normal and healthy on the surface, but college students’ vulnerability is in an uncertain state of intermediate transition.

Emotion recognition research has attracted great interest in various research fields, and electroencephalography (EEG) is considered a promising tool for extracting emotion-related information. However, traditional EEG-based emotion recognition methods ignore the spatial correlation between electrodes. To address this problem, this paper proposes an EEG-based emotion recognition method combining differential entropy feature matrix (DEFM) and 2D-CNN-LSTM. In this work, first, the one-dimensional EEG vector sequence is converted into a two-dimensional grid matrix sequence, which corresponds to the distribution of brain regions of the EEG electrode positions, and can better characterize the spatial correlation between the EEG signals of multiple adjacent electrodes. Then, the EEG signal is divided into equal time windows, and the differential entropy (DE) of each electrode in this time window is calculated, it is combined with a two-dimensional grid matrix and differential entropy to obtain a new data representation that can capture the spatiotemporal correlation of the EEG signal, which is called DEFM. Secondly, we use 2D-CNN-LSTM to accurately identify the emotional categories contained in the EEG signals and finally classify them through the fully connected layer. Experiments are conducted on the widely used DEAP dataset. Experimental results show that the method achieves an average classification accuracy of 91.92% and 92.31% for valence and arousal, respectively. The method performs outstandingly in emotion recognition. This method effectively combines the temporal and spatial correlation of EEG signals, improves the accuracy and robustness of EEG emotion recognition, and has broad application prospects in the field of emotion classification and recognition based on EEG signals.

The experience of maltreatment can impair child development, including changes in the process of emotions recognition, which may result in impairment of social interactions and behavioral disabilities. In order to measure the association between maltreatment and changes on emotion recognition among Brazilian adolescents, the Emotional Recognition Test on Human Faces (ERTHF) was applied to a sample of 50 adolescents who had suffered different intensities and types of abuse. The social and clinical characteristics of the participants were analyzed and, from ERTHF data, the accuracy and response time for the emotion recognition. Males were 60%, with mean age of 13 years and 3 months; 60% were living in shelters. Emotion recognition changes were associated with intensity and types of maltreatment. Physical neglect (48%) was associated with changes in neutral and negative emotions recognition. Emotional neglect (48%) and emotional abuse (46%) were associated with changes in both positive and negative emotions recognition. Physical abuse (38%) was associated with changes in positive emotion recognition only. False recognition of anger was the most common outcome of maltreatment, being associated with physical neglect (p = 0.015) and emotional neglect (p = 0.047). Our results point out to the need to add emotional and facial recognition's rehabilitation interventions to better attend the specific demands of maltreated children and to increase the chances of social and family reintegration.

Multiple sclerosis (MS) may be associated with impaired perception of facial emotions. However, emotion recognition mediated by bodily postures has never been examined in these patients. Moreover, several studies have suggested a relation between emotion recognition impairments and alexithymia. This is in line with the idea that the ability to recognize emotions requires the individuals to be able to understand their own emotions. Despite a deficit in emotion recognition has been observed in MS patients, the association between impaired emotion recognition and alexithymia has received little attention. The aim of this study was, first, to investigate MS patient's abilities to recognize emotions mediated by both facial and bodily expressions and, second, to examine whether any observed deficits in emotions recognition could be explained by the presence of alexithymia. Thirty patients with MS and 30 healthy matched controls performed experimental tasks assessing emotion discrimination and recognition of facial expressions and bodily postures. Moreover, they completed questionnaires evaluating alexithymia, depression, and fatigue. First, facial emotion recognition and, to a lesser extent, bodily emotion recognition can be impaired in MS patients. In particular, patients with higher disability showed an impairment in emotion recognition compared with patients with lower disability and controls. Second, their deficit in emotion recognition was not predicted by alexithymia. Instead, the disease's characteristics and the performance on some cognitive tasks significantly correlated with emotion recognition. Impaired facial emotion recognition is a cognitive signature of MS that is not dependent on alexithymia.

With the continuous development of China's higher education from elite education to mass education, a series of vicious incidents such as college students' suspension from school, dropping out of school and even committing suicide caused by mental health problems are increasing. For the healthy ascent of students, a great deal of higher vocational colleges have set up psychological counseling courses, and applied data mining technology to prevent students' psychological crisis in higher vocational colleges, to ease students' psychological pressure, and to promote students' physical and mental healthy growth in higher vocational colleges. The malignant events caused by college students' psychological crisis have already affected the healthy growth of some students, so we should make clear the sociological significance of college students' psychological crisis intervention and prevent college students from having psychological crisis stress events. Based on data mining technology, aiming at the rising trend of college students' psychological crisis, this paper creates an effective long-term working mechanism of psychological crisis intervention, and finds an effective solution to alleviate and deal with the psychological crisis among college students.