Prerequisites for Affective Signal Processing (ASP)

Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful Affective Signal Processing: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community. Using these directives, a critical analysis of a real-world case is provided. This illustrates that the prerequisites can become a valuable guide for Affective Signal Processing (ASP)

Integration of Wavelet and Recurrence Quantification Analysis in Emotion Recognition of Bilinguals

Background: This study offers a robust framework for the classification of autonomic signals into five affective states during the picture viewing. To this end, the following emotion categories studied: five classes of the arousal-valence plane (5C), three classes of arousal (3A), and three categories of valence (3V). For the first time, the linguality information also incorporated into the recognition procedure. Precisely, the main objective of this paper was to present a fundamental approach for evaluating and classifying the emotions of monolingual and bilingual college students.Methods: Utilizing the nonlinear dynamics, the recurrence quantification measures of the wavelet coefficients extracted. To optimize the feature space, different feature selection approaches, including generalized discriminant analysis (GDA), principal component analysis (PCA), kernel PCA, and linear discriminant analysis (LDA), were examined. Finally, considering linguality information, the classification was performed using a probabilistic neural network (PNN).Results: Using LDA and the PNN, the highest recognition rates of 95.51%, 95.7%, and 95.98% were attained for the 5C, 3A, and 3V, respectively. Considering the linguality information, a further improvement of the classification rates accomplished.Conclusion: The proposed methodology can provide a valuable tool for discriminating affective states in practical applications within the area of human-computer interfaces

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

Guidelines for Mobile Emotion Measurement

ABSTRACT Mobile emotion measurement (MEM) through physiological signals is a promising tool for both experiments and application. We provide 1) an overview of unobtrusive physiological sensors and 2) a review of studies that have tried to infer emotions from physiological signals. This review shows that there is a lack of general standards, low accuracy, and a doubtful validity of the results. To overcome these problems, we provide three guidelines for future research on MEM: validation, triangulation, and a physiology-driven approach. These guidelines enable the embedding of MEM in various professional and consumer settings, as a key factor in our every day life

Guidelines for Mobile Emotion Measurement

ABSTRACT Mobile emotion measurement (MEM) through physiological signals is a promising tool for both experiments and application. We provide 1) an overview of unobtrusive physiological sensors and 2) a review of studies that have tried to infer emotions from physiological signals. This review shows that there is a lack of general standards, low accuracy, and a doubtful validity of the results. To overcome these problems, we provide three guidelines for future research on MEM: validation, triangulation, and a physiology-driven approach. These guidelines enable the embedding of MEM in various professional and consumer settings, as a key factor in our every day life

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition: Wearable Systems, Modeling, and Advanced Biosignal Processing

This thesis aims at investigating how electrophysiological signals related to the autonomic nervous system (ANS) dynamics could be source of reliable and effective markers for mood state recognition and assessment of emotional responses. In-depth methodological and applicative studies of biosignals such as electrocardiogram, electrodermal response, and respiration activity along with information coming from the eyes (gaze points and pupil size variation) were performed. Supported by the current literature, I found that nonlinear signal processing techniques play a crucial role in understanding the underlying ANS physiology and provide important quantifiers of cardiovascular control dynamics with prognostic value in both healthy subjects and patients. Two main applicative scenarios were identified: the former includes a group of healthy subjects who was presented with sets of images gathered from the International Affective Picture System hav- ing five levels of arousal and five levels of valence, including both a neutral reference level. The latter was constituted by bipolar patients who were followed for a period of 90 days during which psychophysical evaluations were performed. In both datasets, standard signal processing techniques as well as nonlinear measures have been taken into account to automatically and accurately recognize the elicited levels of arousal and valence and mood states, respectively. A novel probabilistic approach based on the point-process theory was also successfully applied in order to model and characterize the instantaneous ANS nonlinear dynamics in both healthy subjects and bipolar patients. According to the reported evidences on ANS complex behavior, experimental results demonstrate that an accurate characterization of the elicited affective levels and mood states is viable only when non- linear information are retained. Moreover, I demonstrate that the instantaneous ANS assessment is effective in both healthy subjects and patients. Besides mathematics and signal processing, this thesis also contributes to pragmatic issues such as emotional and mood state mod- eling, elicitation, and noninvasive ANS monitoring. Throughout the dissertation, a critical review on the current state-of-the-art is reported leading to the description of dedicated experimental protocols, reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment

Art and Technology: coherence, connectedness, and the integrative field

Merged with duplicate record 10026.1/690 on 03.04.2017 by CS (TIS)This thesis is a theoretical and practical intervention in the field of art and technology. It proceeds from the re-examination of four specific domains that in the past 40 years have considerably informed the invention of new aesthetic forms. They are: art, science, nature and technology. We have identified that each one of these domains and the way they inform one another reflects the influence of a Western analytical tradition based on fragmentation, dichotomies and dualities. In consequence of this, art of the last decades has suffered from a sort of mechanistic thought which results from a predominantly weary aesthetic model, founded in dualities such as: object/process, form/behaviour, meaning/information. The main question that the present study addresses is how to overcome this predominantly reductionist inheritance and to develop an aesthetic model able to interconnect in an integrative fashion those disparate domains, respective discourses and practices? The answer to this question, developed throughout this thesis, is an aesthetic principle built upon the notions of resonance, coherence and field models, rooted in an integrative view of living organisms based on the theory of biophotons. This constitutes the main contribution of the thesis to new knowledge. The theoretical approach of this thesis is developed upon the revision of the concept of form, supported by a Gestalt analysis as provided by Rudolf Arnheim, and has involved the consideration of the ideas of Gilbert Simondon (the concept of "concretisation") and Vilem Flusser (the concept of "apparatus"), in order to gain a deeper insight into the nature of technology. In conclusion, the practice-based methodology of this thesis has been to develop artworks based on the confluence of living organisms (plants) and artificial systems in order to permit empirical observation and reflection on the proposed theory. The major outcome of the practice is the artwork "Breathing", a hybrid creature made of a living organism (a plant) and an artificial system. The creature responds to its environment through movement, light and the noise of its mechanical parts and interacts with the observer through his/her act of breathing. This work is the result of an investigation into plants as sensitive agents for the creation of art. The intention was to explore new forms of artistic experience through the dialogue of natural and artificial processes