Interactive Robot Learning for Multimodal Emotion Recognition

International audienceInteraction plays a critical role in skills learning for natural communication. In human-robot interaction (HRI), robots can get feedback during the interaction to improve their social abilities. In this context, we propose an interactive robot learning framework using mul-timodal data from thermal facial images and human gait data for online emotion recognition. We also propose a new decision-level fusion method for the multimodal classification using Random Forest (RF) model. Our hybrid online emotion recognition model focuses on the detection of four human emotions (i.e., neutral, happiness, angry, and sadness). After conducting offline training and testing with the hybrid model, the accuracy of the online emotion recognition system is more than 10% lower than the offline one. In order to improve our system, the human verbal feedback is injected into the robot interactive learning. With the new online emotion recognition system, a 12.5% accuracy increase compared with the online system without interactive robot learning is obtained

In-the-wild Facial Expression Recognition in Extreme Poses

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.Comment: Published on ICGIP201

SPEECH EMOTION DETECTION USING MACHINE LEARNING TECHNIQUES

Communication is the key to express one’s thoughts and ideas clearly. Amongst all forms of communication, speech is the most preferred and powerful form of communications in human. The era of the Internet of Things (IoT) is rapidly advancing in bringing more intelligent systems available for everyday use. These applications range from simple wearables and widgets to complex self-driving vehicles and automated systems employed in various fields. Intelligent applications are interactive and require minimum user effort to function, and mostly function on voice-based input. This creates the necessity for these computer applications to completely comprehend human speech. A speech percept can reveal information about the speaker including gender, age, language, and emotion. Several existing speech recognition systems used in IoT applications are integrated with an emotion detection system in order to analyze the emotional state of the speaker. The performance of the emotion detection system can greatly influence the overall performance of the IoT application in many ways and can provide many advantages over the functionalities of these applications. This research presents a speech emotion detection system with improvements over an existing system in terms of data, feature selection, and methodology that aims at classifying speech percepts based on emotions, more accurately

Multimodal Speech Emotion Recognition

Tato práce se zaměřuje na problém Rozpoznávánı́ emocı́, který spadá do třı́dy problémů Zpracovánı́ přirozeného jazyka. Cı́lem této práce bylo vytvořit modely strojového učenı́ na rozpoznánı́ emocı́ z textu a ze zvuku. Práce základně seznámı́ čtenáře s tı́mto problémem, s možnostmi reprezentace emocı́, s dostupnými datovými sadami a s existujı́cı́mi řešenı́mi. Poté se v práci popisujı́ naše navrhnutá řešenı́ pro úlohy Rozpoznávánı́ emocı́ z textu, Rozpoznávánı́ emocı́ ze zvuku a Multimodálnı́ho rozpoznávánı́ emocı́ z řeči. Dále popisujeme experimenty, které jsme provedli, prezentujeme dosažené výsledky těchto experimentů a ukazujeme naše dvě praktické demo aplikace. Dva z našich navrhovaných modelů porazily předchozı́ nejlepšı́ dos-tupné řešenı́ z roku 2018. Všechny experimenty a modely byly naprogramovány v programovacı́m jazyce Python.This work focuses on the Emotion Recognition task, which falls into the Natural Language Processing problems. The goal of this work was to create Machine learning models to recognize emotions from text and audio. The work introduces the problem, possible emotion representations, available datasets, and existing solutions to a reader. It then describes our proposed solutions for Text Emotion Recognition (TER), Speech Emotion Recognition (SER), and Multimodal Speech Emotion Recognition tasks. Further, we describe the experiments we have conducted, present the results of those experiments, and show our two demo practical applications. Two of our proposed models were able to outperform a previous state-of-the-art solution from 2018. All experiments and models were programmed in the Python programming language

User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

Kan bare brukes i forskningssammenheng, ikke kommersielt. Les mer her: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsBuilding predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.acceptedVersio

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Finding Patterns in Biological Parameters

Changes or variation occur in physiological parameters of the body when a person is going through a tough time or he is extremely happy. These changes in physiological parameters can be used for detecting emotions. Emotional computing is a field of Human Computer Interaction(HCI) where we detect human emotions. Emotion recognition based on affective physiological changes is a pattern recognition problem, and selecting specific physiological signals is necessary and helpful to recognize the emotions. In this paper, we have discussed various research papers analysing that how emotions are detected from physiological signals using non-invasive methods. Developers use various Data Mining techniques for developing such results. Heart Rate Variability(HRV), Skin Temperature(ST), Blood Volume Pulse(BVP) are the main highlights as these are key parameters in Physiological signals