Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

A user-centered approach for detecting emotions with low-cost sensors

AbstractDetecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users' biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user's affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance

User emotional interaction processor: a tool to support the development of GUIs through physiological user monitoring

Ever since computers have entered humans' daily lives, the activity between the human and the digital ecosystems has increased. This increase encourages the development of smarter and more user-friendly human-computer interfaces. However, to test these interfaces, the means of interaction have been limited, for the most part restricted to the conventional interface, the "manual" interface, where physical input is required, where participants who test these interfaces use a keyboard, mouse, or a touch screen, and where communication between participants and designers is required. There is another method, which will be applied in this dissertation, which does not require physical input from the participants, which is called Affective Computing. This dissertation presents the development of a tool to support the development of graphical interfaces, based on the monitoring of psychological and physiological aspects of the user (emotions and attention), aiming to improve the experience of the end user, with the ultimate goal of improving the interface design. The development of this tool will be described. The results, provided by designers from an IT company, suggest that the tool is useful but that the optimized interface generated by it still has some flaws. These flaws are mainly related to the lack of consideration of a general context in the interface generation process.Desde que os computadores entraram na vida diária dos humanos, a atividade entre o ecossistema humano e o digital tem aumentado. Este aumento estimula o desenvolvimento de interfaces humano-computador mais inteligentes e apelativas ao utilizador. No entanto, para testar estas interfaces, os meios de interação têm sido limitados, em grande parte restritos à interface convencional, a interface "manual", onde é preciso "input" físico, onde os participantes que testam estas interface, usam um teclado, um rato ou um "touch screen", e onde a comunicação dos participantes com os designers é necessária. Existe outro método, que será aplicado nesta dissertação, que não necessita de "input" físico dos participantes, que se denomina de "Affective Computing". Esta dissertação apresenta o desenvolvimento de uma ferramenta de suporte ao desenvolvimento de interfaces gráficas, baseada na monitorização de aspetos psicológicos e fisiológicos do utilizador (emoções e atenção), visando melhorar a experiência do utilizador final, com o objetivo último de melhorar o design da interface. O desenvolvimento desta ferramenta será descrito. Os resultados, dados por designers de uma empresa de IT, sugerem que esta é útil, mas que a interface otimizada gerada pela mesma tem ainda algumas falhas. Estas falhas estão, principalmente, relacionadas com a ausência de consideração de um contexto geral no processo de geração da interface

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

Framework of controlling 3d virtual human emotional walking using BCI

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique. © 2015 Penerbit UTM Press. All rights reserved

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being