Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Predicting Subjective Sleep Quality Using Objective Measurements in Older Adults

Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support system to estimate SQ and feeling refreshed after sleep using data extracted from an E4 wearable device. Specifically, we processed four raw physiological signals of heart rate variability (HRV), electrodermal activity, body movement, and skin temperature using distinct signal processing methodologies. Following this, we extracted signal-specific features and selected a subset of the features using recursive feature elimination cross validation strategy to maximize the accuracy of SQ classifiers in predicting the SQ of older caregivers. Second, we investigated discovering more effective features in SQ prediction using HRV features which are not only effortlessly measurable but also can reflect sleep stage transitions and some sleep disorders. Evaluation of two interpretable machine learning methodologies and a convolutional neural network (CNN) methodology demonstrated the CNN outperforms by an accuracy of 0.6 in predicting light, medium, and deep SQ. This outcome verified the capability of using HRV features measurable by easy-to-use wearable devices, in predicting SQ. Finally, we scrutinized daytime sleepiness risk factors as a sign of abnormal SQ from four perspectives: sleep fragmented, sleep propensity, sleep resilience, and non-restorative sleep. The analysis demonstrates distinguishability of the main risk factors of excessive daytime sleepiness (EDS) between patients suffering from fragmented sleep (e.g. apnea) and sleep propensity. We identified the average area under oxygen desaturation curve corresponds to apnea/hypopnea event as a disease-specific risk factor of abnormal SQ. Our further daytime sleepiness prediction demonstrated the significant role of the founded disease-specific risk factor as well

Autonomous Assessment of Videogame Difficulty Using Physiological Signals

Given the well-explored relation between challenge and involvement in a task, (e.g., as described in Csikszentmihalyi’s theory of flow), it could be argued that the presence of challenge in videogames is a core element that shapes player experiences and should, therefore, be matched to the player’s skills and attitude towards the game. However, handling videogame difficulty, is a challenging problem in game design, as too easy a task can lead to boredom and too hard can lead to frustration. Thus, by exploring the relationship between difficulty and emotion, the current work intends to propose an artificial intelligence model that autonomously predicts difficulty according to the set of emotions elicited in the player. To test the validity of this approach, we developed a simple puzzle-based Virtual Reality (VR) videogame, based on the Trail Making Test (TMT), and whose objective was to elicit different emotions according to three levels of difficulty. A study was carried out in which physiological responses as well as player self- reports were collected during gameplay. Statistical analysis of the self-reports showed that different levels of experience with either VR or videogames didn’t have a measurable impact on how players performed during the three levels. Additionally, the self-assessed emotional ratings indicated that playing the game at different difficulty levels gave rise to different emotional states. Next, classification using a Support Vector Machine (SVM) was performed to verify if it was possible to detect difficulty considering the physiological responses associated with the elicited emotions. Results report an overall F1-score of 68% in detecting the three levels of difficulty, which verifies the effectiveness of the adopted methodology and encourages further research with a larger dataset.Dada a relação bem explorada entre desafio e envolvimento numa tarefa (p. ex., con- forme descrito na teoria do fluxo de Csikszentmihalyi), pode-se argumentar que a pre- sença de desafio em videojogos é um elemento central que molda a experiência do jogador e deve, portanto, ser compatível com as habilidades e a atitude que jogador exibe perante o jogo. No entanto, saber como lidar com a dificuldade de um videojogo é um problema desafiante no design de jogos, pois uma tarefa muito fácil pode gerar tédio e muito di- fícil pode levar à frustração. Assim, ao explorar a relação entre dificuldade e emoção, o presente trabalho pretende propor um modelo de inteligência artificial que preveja de forma autônoma a dificuldade de acordo com o conjunto de emoções elicitadas no jogador. Para testar a validade desta abordagem, desenvolveu-se um jogo de puzzle em Realidade Virtual (RV), baseado no Trail Making Test (TMT), e cujo objetivo era elicitar diferentes emoções tendo em conta três níveis de dificuldade. Foi realizado um estudo no qual se recolheram as respostas fisiológicas, juntamente com os autorrelatos dos jogado- res, durante o jogo. A análise estatística dos autorelatos mostrou que diferentes níveis de experiência com RV ou videojogos não tiveram um impacto mensurável no desempenho dos jogadores durante os três níveis. Além disso, as respostas emocionais auto-avaliadas indicaram que jogar o jogo em diferentes níveis de dificuldade deu origem a diferentes estados emocionais. Em seguida, foi realizada a classificação por intermédio de uma Má- quina de Vetores de Suporte (SVM) para verificar se era possível detectar dificuldade, considerando as respostas fisiológicas associadas às emoções elicitadas. Os resultados re- latam um F1-score geral de 68% na detecção dos três níveis de dificuldade, o que verifica a eficácia da metodologia adotada e incentiva novas pesquisas com um conjunto de dados maior

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art

Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

[EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). 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