Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli

Choi, Seul Gi; Cho, Sung Bae

doi:10.1016/j.neucom.2018.09.109

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…In [ 60 ], based on individual emotion recognition with a Bayesian network, an approach to estimate group emotion from face expressions and prosodic information was proposed. Similarly, with a Bayesian network and individual facial expression recognition, but combined with environmental conditions (e.g., light, temperature), in [ 61 ], an approach to estimate the group emotion to then produce appropriate stimuli to induce a target group emotion was presented. Furthermore, from individual facial expressions, in [ 62 ], a system to recognise group emotion for an entertainment robot was described.…”

Section: Related Workmentioning

confidence: 99%

Group Emotion Detection Based on Social Robot Perception

Quiroz

Patiño

Díaz-Amado

et al. 2022

Sensors

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Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human–Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.

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Section: Related Workmentioning

confidence: 99%

Group Emotion Detection Based on Social Robot Perception

Quiroz

Patiño

Díaz-Amado

et al. 2022

Sensors

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“…In addition to the directions mentioned above, the combination of Bayesian methods and reinforcement learning has a broader range of applications, including recommendation systems Dürr et al (2021), cyber security Allen et al (2018), automatic driving and wireless on-board systems Gharaee et al (2021); Liang et al (2021), biomedical science Imani and Braga-Neto (2018); Choi and Cho (2020); Rathore and Samant (2021), blockchain Asheralieva and Niyato (2020), the industrial spectrum Liu et al (2020), control systems Ouyang et al (2019). Application needs and the main objective of algorithm research complement each other.…”

Section: Applicationsmentioning

confidence: 99%

Towards Uncertainty in Decision: A Survey on Recent Advances and Challenges in Bayesian Reinforcement Learning

Wang

Meng

Zhou

et al. 2022

Preprint

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Reinforcement learning is a research paradigm that is commonly utilized to tackle problems involving sequential decision-making. Agents learn optimum policy from samples generated by interacting with uncertain environments. Bayesian reinforcement learning presents the uncertainty measure of probability significance based on sequential decision-making, which plays a proper auxiliary function in the optimization of agent control policies, attracting a lot of study attention. This paper provides an overview of contemporary Bayesian and reinforcement learning methods in this setting. We begin by outlining the many types of reinforcement learning and Bayesian approaches, as well as their characteristics. Then we review the most sophisticated Bayesian reinforcement learning approaches from the perspective of uncertainty measurement and control, as well as summarize and discuss various challenges and related research in its expansion to complicated control tasks. Applying the Bayesian technique with reinforcement learning in diverse disciplines are then discussed. Finally, we highlight the current challenges of Bayesian reinforcement learning and future research topics that might help promote Bayesian reinforcement learning.

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“…Redes bayesianas [36] Electroencefalograma EEG Algoritmo K-means KNN, Bayesiano ingenuo NB, Máquina de vectores SVM y Bosque aleatorio RF [37] Seguimiento ocular mediante cámara IR, Sensor proximidad, Unidad de medición inercial IMU Máquina de vectores SVM [38] Seguimiento ocular: tamaño de la pupila, la posición de la pupila y la velocidad de movimiento del ojo Clasificador Red Neuronal Artificial [39] Electroencefalograma EEG, Respuesta galvánica de la piel GSR y Variabilidad de la frecuencia cardiaca HRV.…”

Section: Luz Temperaturaunclassified

“…de los usuarios, y algunos algoritmos de inteligencia artificial como SVM y modelos de redes neuronales convolusionales. De estos trabajos destacan: i) la detección de emociones y la modificación del entorno para mejorar el estado emocional de usuario usando dispositivos IoT [24,26,34,36]; y, ii) La detección de emociones y estrés por medio de dispositivos IoT analizando rasgos faciales o patrones de movimiento del usuario [20,27].…”

Section: Discusión Y Conclusionesunclassified

Prevención del síndrome de Burnout mediante programación neurolingüística soportada por la Web de las Cosas: Mapeo sistemático

2020

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En esta investigación, mediante la exploración de la literatura, se realizó un mapeo sistemático sobre la aplicación de técnicas de programación neurolingüística (PNL) soportadas en la Web de las Cosas (WoT) para prevenir el síndrome de Burnout. Dicho síndrome es un tipo de estrés laboral que causa agotamiento físico, mental y emocional, generando una incapacidad para trabajar, dado que es un proceso paulatino en el cual el trabajador pierde interés por sus tareas, carece de sentido de responsabilidad y puede generar profundas depresiones. En los estudios encontrados se destaca el uso de la WoT para la detección de emociones y estrés laboral, para ello sobresale el uso de sensores capaces de medir Respuesta galvánica de la piel GSR, Frecuencia cardiaca HR, Fotopletismografía PPG, Electrocardiograma ECG, Cámaras, Micrófonos y Microprocesadores de bajo costo, así como la utilización de Inteligencia Artificial para procesar estos datos, entre las técnicas y los algoritmos más usados destacan Máquinas de Vectores de Soporte SVM, K-vecino más cercano y clasificador Naive Bayes. En los trabajos en los que se detectan emociones o estrés laboral son muy pocos los que intentan alterar el entorno mental o ambiental del usuario para llevarlo a una emoción positiva o disminuir el estrés. Se evidenció la posibilidad de la utilización de técnicas de PNL en la prevención del síndrome de Burnout. Sin embargo, no se encontró ningún trabajo que relacionara la WoT como soporte a las técnicas PNL para prevenir el síndrome de Burnout, lo cual se considera como una oportunidad de investigación en estos campos.

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Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli

Cited by 6 publications

References 17 publications

Group Emotion Detection Based on Social Robot Perception

Group Emotion Detection Based on Social Robot Perception

Towards Uncertainty in Decision: A Survey on Recent Advances and Challenges in Bayesian Reinforcement Learning

Prevención del síndrome de Burnout mediante programación neurolingüística soportada por la Web de las Cosas: Mapeo sistemático

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