Techniques in Pattern Recognition for School Bullying Prevention: Review and Outlook

School bullying is a serious problem among teenagers. With the development of sensor technology and pattern recognition algorithms, several approaches for detecting school bullying have been developed, namely speech emotion recognition, mental stress recog- nition, and activity recognition. This paper reviews some related work and makes some comparisons among these three aspects. The paper analyzes commonly used features and classifiers, and describes some examples. The Gaussian Mixture Model and the Double Threshold classifiers provided high accuracies in many experiments. By using a combined architecture of classifiers, the results could be further improved. According to the results of the experiments, the six basic emotions, high mental stress and irregular movements can be recognized with high accuracies. So the three types of pattern recognition can be used for school bullying detection effectively. And these techniques can be used on consumer devices such as smartphones to protect teenagers

Physical Violence Detection for Preventing School Bullying

School bullying is a serious problem among teenagers, causing depression, dropping out of school, or even suicide. It is thus important to develop antibullying methods. This paper proposes a physical bullying detection method based on activity recognition. The architecture of the physical violence detection system is described, and a Fuzzy Multithreshold classifier is developed to detect physical bullying behaviour, including pushing, hitting, and shaking. Importantly, the application has the capability of distinguishing these types of behaviour from such everyday activities as running, walking, falling, or doing push-ups. To accomplish this, the method uses acceleration and gyro signals. Experimental data were gathered by role playing school bullying scenarios and by doing daily-life activities. The simulations achieved an average classification accuracy of 92, which is a promising result for smartphone-based detection of physical bullying

Emotion Recognition by Heart Rate Variability

Background: Emotion plays an important role when people face difficult social problems in their daily activities. This study explores the application of sensors and mobile technologies to detect and recognize school bullying. Many databases offer data for emotion recognition research. One of these is the Mahnob-HCI-Tagging database, which yields a baseline accuracy for emotion recognition based on EEG, eye gaze, and a combination of EEG and eye gaze. Because EEG and eye gaze are not suitable for emotion recognition in the mobile gadget environment, it is interesting to investigate other physiological signals individually, such as ECG, galvanic skin conductance (GSR), body temperature and respiration rate. Objective: This paper focussed on the ECG signal and, more specifically, on heart rate variability (HRV), derived from ECG, to identify certain standard features used in emotion recognition. Instead of using discrete emotions as labels, we transferred emotions, such as fear, anger, happiness and anxiety, to an arousal-valence space. Results:For arousal and valence based on HRV, the baselines are 47.69 and 42.55, respectively, while those for arousal and valence in all physiological signals were 46.2 and 45.5, respectively. The most challenging label in this experiment turned out to be #65533neutral#65533 in the valence scale, as the SVM classified all results as either #65533unpleasant#65533 or #65533pleasant#65533. Conclusion: This work provided a baseline for emotion recognition research based on ECG signals. It also encourages experimental trials using GSR, body temperature and respiration rate individually

Enhancement of emotion recogniton using feature fusion and the neighborhood components analysis

Abstract Feature fusion is a common approach to improve the accuracy of the system. Several attemps have been made using this approach on the Mahnob-HCI database for affective recognition, achieving 76% and 68% for valence and arousal respectively as the highest achievements. This study aimed to improve the baselines for both valence and arousal using feature fusion of HRV-based, which used the standard Heart Rate Variability analysis, standardized to mean/standard deviation and normalized to [-1,1], and cvxEDA-based feature, calculated based on a convex optimization approach, to get the new baselines for this database. The selected features, after applying the sequential forward floating search (SFFS), were enhanced by the Neighborhood Component Analysis and fed to kNN classifier to solve 3-class classification problem, validated using leave-one-out (LOO), leave-one-subject-out (LOSO), and 10-fold cross validation methods. The standardized HRV-based features were not selected during the SFFS method, leaving feature fusion from normalized HRV-based and cvxEDA-based features only. The results were compared to previous studies using both single- and multi-modality. Applying the NCA enhanced the features such that the performances in valence set new baselines: 82.4% (LOO validation), 79.6% (10-fold cross validation), and 81.9% (LOSO validation), enhanced the best achievement from both single- and multi-modality. For arousal, the performances were 78.3%, 78.7%, and 77.7% for LOO, LOSO, and 10-fold cross validations respectively. They outperformed the best achievement using feature fusion but could not enhance the performance in single-modality study using cvxEDA-based feature. Some future works include utilizing other feature extraction methods and using more sophisticated classifier other than the simple kNN

Comparing features from ECG pattern and HRV analysis for emotion recognition system

Abstract We propose new features for emotion recognition from short ECG signals. The features represent the statistical distribution of dominant frequencies, calculated using spectrogram analysis of intrinsic mode function after applying the bivariate empirical mode decomposition to ECG. KNN was used to classify emotions in valence and arousal for a 3-class problem (low-medium-high). Using ECG from the Mahnob-HCI database, the average accuracies for valence and arousal were 55.8% and 59.7% respectively with 10-fold cross validation. The accuracies using features from standard Heart Rate Variability analysis were 42.6% and 47.7% for valence and arousal respectively for the 3-class problem. These features were also tested using subject-independent validation, achieving an accuracy of 59.2% for valence and 58.7% for arousal. The proposed features also showed better performance compared to features based on statistical distribution of instantaneous frequency, calculated using Hilbert transform of intrinsic mode function after applying standard empirical mode decomposition and bivariate empirical mode decomposition to ECG. We conclude that the proposed features offer a promising approach to emotion recognition based on short ECG signals. The proposed features could be potentially used also in applications in which it is important to detect quickly any changes in emotional state

Enhancing emotion recognition from ECG signals using supervised dimensionality reduction

Abstract Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it also can improve the performance of the system. This paper explores supervised dimensionality reduction, LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database. It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence was improved from 55.8% to 64.1%, and for arousal from 59.7% to 66.1% using 10-fold cross validation after transforming the features with projection matrices from NCA. For LOSO validation, there is no significant improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%