Prerequisites for Affective Signal Processing (ASP) - Part III

This is the third part in a series on prerequisites for affective signal processing (ASP). So far, six prerequisites were identified: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community (van den Broek et al., 2009) and identification of users and theoretical specification (van den Broek et al., 2010). Here, two additional prerequisites are identified: integration of biosignals, and physical characteristics

Prerequisites for Affective Signal Processing (ASP) - Part V: A response to comments and suggestions

In four papers, a set of eleven prerequisites for affective signal processing (ASP) were identified (van den Broek et al., 2010): validation, triangulation, a physiology-driven approach, contributions of the signal processing community, identification of users, theoretical specification, integration of biosignals, physical characteristics, historical perspective, temporal construction, and real-world baselines. Additionally, a review (in two parts) of affective computing was provided. Initiated by the reactions on these four papers, we now present: i) an extension of the review, ii) a post-hoc analysis based on the eleven prerequisites of Picard et al.(2001), and iii) a more detailed discussion and illustrations of temporal aspects with ASP

Towards an artificial therapy assistant: Measuring excessive stress from speech

The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling\ud sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story, different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain 70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective measurement of stress in speech. As such, the foundation for an Artificial Therapeutic Agent is laid, capable of assisting therapists through an objective measurement of experienced stress

Affective computing: a reverence for a century of research

To bring affective computing a leap forward, it is best to start with a step back. A century of research has been conducted on topics, which are crucial for affective computing. Understanding this vast amount of research will accelerate progress on affective computing. Therefore, this article provides an overview of the history of affective computing. The complexity of affect will be described by discussing i) the relation between body and mind, ii) cognitive processes (i.e., attention, memory, and decision making), and iii) affective computing's I/O. Subsequently, definitions are provided of affect and related constructs (i.e., emotion, mood, interpersonal stances, attitude, and personality traits) and of affective computing. Perhaps when these elements are embraced by the community of affective computing, it will us a step closer in bridging its semantic gap. © 2012 Springer-Verlag

Heritability of aggression following social evaluation in middle childhood: An fMRI study.

Middle childhood marks an important phase for developing and maintaining social relations. At the same time, this phase is marked by a gap in our knowledge of the genetic and environmental influences on brain responses to social feedback and their relation to behavioral aggression. In a large developmental twin sample (509 7- to 9-year-olds), the heritability and neural underpinnings of behavioral aggression following social evaluation were investigated, using the Social Network Aggression Task (SNAT). Participants viewed pictures of peers that gave positive, neutral, or negative feedback to the participant’s profile. Next, participants could blast a loud noise toward the peer as an index of aggression. Genetic modeling revealed that aggression following negative feedback was influenced by both genetics and environmental (shared as well as unique environment). On a neural level (n 5 385), the anterior insula and anterior cingulate cortex gyrus (ACCg) responded to both positive and negative feedback, suggesting they signal for social salience cues. The medial prefrontal cortex (mPFC) and inferior frontal gyrus (IFG) were specifically activated during negative feedback, whereas positive feedback resulted in increased activation in caudate, supplementary motor cortex (SMA), and dorsolateral prefrontal cortex (DLPFC). Decreased SMA and DLPFC activation during negative feedback was associated with more aggressive behavior aft

UTPA Catalog 1994-1996

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UTB/TSC Undergraduate Catalog 1997-1998

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Connecting people through physiosocial technology

Social connectedness is one of the most important predictors of health and well-being. The goal of this dissertation is to investigate technologies that can support social connectedness. Such technologies can build upon the notion that disclosing emotional information has a strong positive influence on social connectedness. As physiological signals are strongly related to emotions, they might provide a solid base for emotion communication technologies. Moreover, physiological signals are largely lacking in unmediated communication, have been used successfully by machines to recognize emotions, and can be measured relatively unobtrusively with wearable sensors. Therefore, this doctoral dissertation examines the following research question: How can we use physiological signals in affective technology to improve social connectedness? First, a series of experiments was conducted to investigate if computer interpretations of physiological signals can be used to automatically communicate emotions and improve social connectedness (Chapters 2 and 3). The results of these experiments showed that computers can be more accurate at recognizing emotions than humans are. Physiological signals turned out to be the most effective information source for machine emotion recognition. One advantage of machine based emotion recognition for communication technology may be the increase in the rate at which emotions can be communicated. As expected, experiments showed that increases in the number of communicated emotions increased feelings of closeness between interacting people. Nonetheless, these effects on feelings of closeness are limited if users attribute the cause of the increases in communicated emotions to the technology and not to their interaction partner. Therefore, I discuss several possibilities to incorporate emotion recognition technologies in applications in such a way that users attribute the communication to their interaction partner. Instead of using machines to interpret physiological signals, the signals can also be represented to a user directly. This way, the interpretation of the signal is left to be done by the user. To explore this, I conducted several studies that employed heartbeat representations as a direct physiological communication signal. These studies showed that people can interpret such signals in terms of emotions (Chapter 4) and that perceiving someone's heartbeat increases feelings of closeness between the perceiver and sender of the signal (Chapter 5). Finally, we used a field study (Chapter 6) to investigate the potential of heartbeat communication mechanisms in practice. This again confirmed that heartbeat can provide an intimate connection to another person, showing the potential for communicating physiological signals directly to improve connectedness. The last part of the dissertation builds upon the notion that empathy has positive influences on social connectedness. Therefore, I developed a framework for empathic computing that employed automated empathy measurement based on physiological signals (Chapter 7). This framework was applied in a system that can train empathy (Chapter 8). The results showed that providing users frequent feedback about their physiological synchronization with others can help them to improve empathy as measured through self-report and physiological synchronization. In turn, this improves understanding of the other and helps people to signal validation and caring, which are types of communication that improve social connectedness. Taking the results presented in this dissertation together, I argue that physiological signals form a promising modality to apply in communication technology (Chapter 9). This dissertation provides a basis for future communication applications that aim to improve social connectedness