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Introduction

1.1 Introduction

1.1 Introduction

Originally, computers were invented for highly trained operators, to help them do massive numbers of calculations [149, 610]. However, this origin dates from the first half of the previous century, and much has changed since. nowadays, everybody uses them in one of their many guises. Whereas previously computers were stationary entities the size of a room, today we are in touch with various types of computers throughout our normal daily lives, including our smart phones [3, 122, 135, 381, 460, 610, 713]. Computation is on track to become even smaller and more pervasive. For example, microrobots can already flow through your blood vessels and identify and treat physical damage [2, 165, 214, 479]. Moreover, from dedicated specialized machines, computers have become our window to both the world and our social life [145, 472, 532].

Computers are slowly becoming dressed, huggable, and tangible and our reality will become augmented by virtual realities [50, 594]. Artificial entities are becoming personalized and are expected to understand more and more of their users’ feelings, emotions, and moods [286, 594, 671]. Consequently, concepts such as emotions, that were originally the playing field of philosophers, sociologists, and psychologists [302] have become entangled in computer science as well [210]. This topic was baptized affective computing by Rosalind W. Picard [520, 521]. Picard identified biosignals as an important covert channel to capture human emotions, in addition to channels such as speech and computer vision.

Biosignals (or physiological signals) can be defined as (bio)electrical signals recorded on the body surface, although both non-electrical biosignals and invasive recording techniques exist as well. These bio(electrical) signals are related to ionic processes that arise as a result of electrochemical activity of cells of specialized tissue (e.g., the nervous system). This results in (changes in) electric currents produced by the sum of electrical potential differences across the tissue. This property is similar regardless of the part of the body the cells are located (e.g., the heart, muscles, or the brain) [245, 620]. For an overview of biosignals used for affective computing, I refer to Table 1.1.

There have been many studies that have investigated the use of biosignals for affective computing in the last decade. In Section 1.3 an overview of relevant handbooks will be provided and in Chapter 2 an exhaustive review of research articles will be provided. The handbooks and articles have in common that they illustrate, as I will also show later on (i.e., Chapter 2), that the results on affective computing have been slightly disappointing at best. Hence, I believe a careful examination of the current state-of-the-art can help to provide new insights for future progress. In sum, the goal of this monograph is to i) review the progress made on the processing of biosignals related to emotions (i.e., Affective Signal Processing (ASP) ), ii) conduct necessary additional research, and iii) provide guidelines on issues that need to be tackled in order to improve ASP’s performance.

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