Recognition of emotions using Kinects

Abstract Emotion recognition can improve the quality of patient care, product development and human-machine interaction. Psychological studies indicate that emotional state can be expressed in the way people walk, and the human gait can be used to reveal a person's emotional state. This paper proposes a novel method to do emotion recognition by using Microsoft Kinect to record gait patterns and train machine learning algorithms for emotion recognition. 59 subjects are recruited, and their gait patterns are recorded by two Kinect cameras. Joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation are used for data preprocessing. We run Fourier transformation to extract features from the gait patterns and utilize Principal Component Analysis(PCA) for feature selection. By using NaiveBayes, RandomForests, LibSVM and SMO classifiers, the accuracy of recognition between natural and angry emotions can reach 80%, and the accuracy of recognition between natural and happy emotions can reach above 70%. The result indicates that Kinect can be used in the recognition of emotions with fairly well performance

ClassRoom VR-Motion Capture (CVR-MC): a VR game to improve corporal expression in secondary-school teachers

Trabajo de Fin de Grado en Desarrollo de Videojuegos y en Ingeniería del Software, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021.Contar con docentes cualificados y experimentados al frente de las aulas es imprescindible en cualquier sociedad. Sin embargo, la realidad es que no todos los docentes han podido disfrutar de una preparación práctica que les permita gestionar situaciones con las que no están familiarizados. Un ejemplo de estas, son las situaciones conflictivas. La falta de entornos seguros que permitan estas prácticas hace que se cree un problema insostenible al que debemos dar solución cuanto antes. A la hora de afrontar un conflicto es realmente importante ser asertivo y mostrar corporalmente lo que queremos decir con nuestras palabras. La comunicación efectiva es una asignatura pendiente para la mayoría de docentes que comienzan su carrera profesional. Sin embargo, es algo que puede enseñarse y que mejora las habilidades de resolución de conflictos. En este trabajo, presentamos la implementación y evaluación de ClassRoom VR-Motion Capture, una herramienta que permite a los docentes noveles situarse al frente de una clase donde se producen situaciones disruptivas y reaccionar ante ellas de manera segura. Durante la ejecución se recogerán datos relativos al lenguaje no verbal como son: los cambios en la tonalidad de la voz, los gestos y posiciones, la distancia entre interlocutores y palabras clave. Con esta información estimaremos la emoción con la que se relaciona el lenguaje no verbal del usuario. Además, tras su actuación recibiría un feedback valorando las decisiones tomadas para resolver el conflicto y un análisis del lenguaje no verbal empleado junto con las emociones estimadas. Tras el desarrollo de la aplicación realizamos un experimento junto con 14 profesionales del sector de la educación de Barcelona. En este documento se describe la prueba realizada con ClassRoom VR-Motion Capture con la intención de responder a dos preguntas de investigación. ¿Es factible utilizar el sistema CVR-MC en la formación docente para contribuir al aprendizaje de competencias comunicativas de gestión de clima de aula? Por otra parte, ¿es posible capturar el lenguaje no verbal y las emociones que manifiestan los participantes durante la simulación? ¿Se corresponden con las que manifiestan en un entorno real? De esta prueba concluimos que es un entorno amigable, seguro y factible para la preparación de futuros docentes. Sin embargo, hacemos una reflexión sobre cómo nuestro lenguaje no verbal y por lo tanto, las emociones que transmitimos, no concuerdan en entornos reales y entornos virtuales.Nowadays having qualified and experienced teachers in school classrooms is considered to be of the highest priority in any society. Unlikely this is far from reality because most teachers confess, they don´t have received enough practical training to manage disruptive situations in the classroom. The lack of safe environments which allow these practices creates an unsustainable problem which must be solved as soon as possible. Facing conflict is usually a hard task and it is extremely important to be assertive and to show with your body what we want to say with our words. Effective communication seems to be an enormous issue for most teachers beginning their professional careers. Fortunately, it can be taught and also could improve conflict resolution skills. In this project, we show not just the implementation but also the evaluation of Classroom VR-Motion Capture, an application which allows new teachers to experience different disruptive situations in a classroom and react to them in a safe environment. During the simulation, data related to non-verbal language will be collected, such as: voice tone variations, gestures and positions, distance between interlocutors (proxemia) and keywords. As a result of this procedure, we will estimate the emotion related to the user’s non-verbal language. In addition, after their performance, they will receive feedback about the decisions made to resolve the conflict, an analysis of the non-verbal language used and the estimated emotions. Coming up next to developing the application, we carried out an experiment where took part 14 education professionals from Barcelona. This document describes the test carried out with ClassRoom VR-Motion Capture for answering two research questions. Firstly, is it possible to use CVR-MC system in teacher training to improve the communications skills for classroom climate management? Secondly, is it possible to capture non-verbal language and their relevant emotions which are expressed by the participants during the simulation? Do they match with those expressed in real environments? Thanks to this test we conclude that ClassRoom VR-Motion Capture is a friendly, safe and feasible environment for training future teachers. However, we observed that our nonverbal language and therefore the emotions we transmit, do not match in real and virtual environments.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu