Subjective and objective measures

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.info:eu-repo/semantics/acceptedVersio

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Machine learning-based affect detection within the context of human-horse interaction

This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detection and interpretation of human emotion, an application that could significantly benefit quantitative studies in the field of animal assisted therapy. The chapter offers a thorough description, an experimental design, and experimental results on the use of physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals, for the creation and evaluation of machine learning models for the prediction of the emotional state of an individual during interaction with horses

EMERALD—Exercise Monitoring Emotional Assistant

The increase in the elderly population in today’s society entails the need for new policies to maintain an adequate level of care without excessively increasing social spending. One of the possible options is to promote home care for the elderly. In this sense, this paper introduces a personal assistant designed to help elderly people in their activities of daily living. This system, called EMERALD, is comprised of a sensing platform and different mechanisms for emotion detection and decision-making that combined produces a cognitive assistant that engages users in Active Aging. The contribution of the paper is twofold—on the one hand, the integration of low-cost sensors that among other characteristics allows for detecting the emotional state of the user at an affordable cost; on the other hand, an automatic activity suggestion module that engages the users, mainly oriented to the elderly, in a healthy lifestyle. Moreover, by continuously correcting the system using the on-line monitoring carried out through the sensors integrated in the system, the system is personalized, and, in broad terms, emotionally intelligent. A functional prototype is being currently tested in a daycare centre in the northern area of Portugal where preliminary tests show positive results.This research was partially funded by the Fundacao para a Ciencia e Tecnologia (FCT) within the projects UID/CEC/00319/2019 and Post-Doc Grant SFRH/BPD/102696/2014 (Angelo Costa). This work is also partially funded by the MINECO/FEDER TIN2015-65515-C4-1-R and RISEWISE (RISEWomen with disabilities In Social Engagement) EU project under Agreement No. 690874.info:eu-repo/semantics/publishedVersio

Design of an artificial neural network and feature extraction to identify arrhythmias from ECG

This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.info:eu-repo/semantics/acceptedVersio