- •Stellingen
- •Propositions
- •List of Figures
- •List of Tables
- •1 Introduction
- •Introduction
- •Affect, emotion, and related constructs
- •Affective Computing: A concise overview
- •The closed loop model
- •Three disciplines
- •Human-Computer Interaction (HCI)
- •Health Informatics
- •Three disciplines, one family
- •Outline
- •2 A review of Affective Computing
- •Introduction
- •Vision
- •Speech
- •Biosignals
- •A review
- •Time for a change
- •3 Statistical moments as signal features
- •Introduction
- •Emotion
- •Measures of affect
- •Affective wearables
- •Experiment
- •Participants
- •Equipment and materials
- •Procedure
- •Data reduction
- •Results
- •Discussion
- •Comparison with the literature
- •Use in products
- •4 Time windows and event-related responses
- •Introduction
- •Data reduction
- •Results
- •Mapping events on signals
- •Discussion and conclusion
- •Interpreting the signals measured
- •Looking back and forth
- •5 Emotion models, environment, personality, and demographics
- •Introduction
- •Emotions
- •Modeling emotion
- •Ubiquitous signals of emotion
- •Method
- •Participants
- •International Affective Picture System (IAPS)
- •Digital Rating System (DRS)
- •Signal processing
- •Signal selection
- •Speech signal
- •Heart rate variability (HRV) extraction
- •Normalization
- •Results
- •Considerations with the analysis
- •The (dimensional) valence-arousal (VA) model
- •The six basic emotions
- •The valence-arousal (VA) model versus basic emotions
- •Discussion
- •Conclusion
- •6 Static versus dynamic stimuli
- •Introduction
- •Emotion
- •Method
- •Preparation for analysis
- •Results
- •Considerations with the analysis
- •The (dimensional) valence-arousal (VA) model
- •The six basic emotions
- •The valence-arousal (VA) model versus basic emotions
- •Static versus dynamic stimuli
- •Conclusion
- •IV. Towards affective computing
- •Introduction
- •Data set
- •Procedure
- •Preprocessing
- •Normalization
- •Baseline matrix
- •Feature selection
- •k-Nearest Neighbors (k-NN)
- •Support vector machines (SVM)
- •Multi-Layer Perceptron (MLP) neural network
- •Discussion
- •Conclusions
- •8 Two clinical case studies on bimodal health-related stress assessment
- •Introduction
- •Post-Traumatic Stress Disorder (PTSD)
- •Storytelling and reliving the past
- •Emotion detection by means of speech signal analysis
- •The Subjective Unit of Distress (SUD)
- •Design and procedure
- •Features extracted from the speech signal
- •Results
- •Results of the Stress-Provoking Story (SPS) sessions
- •Results of the Re-Living (RL) sessions
- •Overview of the features
- •Discussion
- •Stress-Provoking Stories (SPS) study
- •Re-Living (RL) study
- •Stress-Provoking Stories (SPS) versus Re-Living (RL)
- •Conclusions
- •9 Cross-validation of bimodal health-related stress assessment
- •Introduction
- •Speech signal processing
- •Outlier removal
- •Parameter selection
- •Dimensionality Reduction
- •k-Nearest Neighbors (k-NN)
- •Support vector machines (SVM)
- •Multi-Layer Perceptron (MLP) neural network
- •Results
- •Cross-validation
- •Assessment of the experimental design
- •Discussion
- •Conclusion
- •10 Guidelines for ASP
- •Introduction
- •Signal processing guidelines
- •Physical sensing characteristics
- •Temporal construction
- •Normalization
- •Context
- •Pattern recognition guidelines
- •Validation
- •Triangulation
- •Conclusion
- •11 Discussion
- •Introduction
- •Hot topics: On the value of this monograph
- •Applications: Here and now!
- •TV experience
- •Knowledge representations
- •Computer-Aided Diagnosis (CAD)
- •Visions of the future
- •Robot nannies
- •Digital Human Model
- •Conclusion
- •Bibliography
- •Summary
- •Samenvatting
- •Dankwoord
- •Curriculum Vitae
- •Publications and Patents: A selection
- •Publications
- •Patents
- •SIKS Dissertation Series
11 Discussion
is that biometrics introduces the risk of social exclusion [719], which would increase with the introduction of biosignal-based biometrics, as it enables the extraction of much more information than solely traditional biometric data. This makes the balance between intelligence (e.g., AmI) and privacy even more sensitive than with traditional biometrics (cf. [684]). Although there is still a long way to go, it will be interesting to see whether biometrics and ASP will indeed merge and evolve to a DHM and if so, what consequences this will have for our lives. Human dignity should be a leading denominator in future research on both DHM and ASP [113, 490], perhaps even more than anything else.
11.7 Conclusion
ASP should be considered as a crucial element for HCI, AI, and health informatics. While machines evolve rapidly, incorporating more and more sensors, and receiving more and more autonomy, the interaction with their users has become more delicate than ever before. Users are increasingly starting to demand that computing devices should understand them. Bringing affective processes into AI is said to be the field’s missing link. ASP can play a crucial role in this process. ASP-based technology will prove to be invaluable in supporting our health and well-being. However, the field’s progress lays far behind science’s (initial) expectations and results are disappointing. This monograph has explored several of ASP’s dimensions and as such contributed to the existing body of knowledge. Additionally, a set of guidelines has been presented to provide a concise set of research methods and standards to the field of ASP. I hope these guidelines may boost the field’s progress.
With this monograph representing the work of just a few years, only a humble step has been made. But now that ASP has both academics’ and industry’s attention, its progress will be accelerated. Perhaps with the lessons learned throughout this monograph and the guidelines it provides, this book will become a reference for ASP and aid its research and development. Then, in time, its progress will prove difficult to stop and a new landscape will arise for humanity in which even ethical concerns will need to be redefined to retain their value. Today this may all sound like science fiction; however, there will be a tomorrow in which it will not, in which our being will be redefined and perhaps this tomorrow will come even sooner than we may now expect.
Undoubtedly, in time, the progress of ASP will prove to be difficult to stop. ASP will (possibly) unnoticeably penetrate our everyday lives. Agents and avatars augmented with ASP will support us in our work, will increase our level of mindfulness, and improve the quality or our lives. And, as envisioned, ASP will prove to be the essential key in the fusion of man and his technology. More importantly, hopefully it can also serve as an interface to help people understanding each other, to help them see what they have in common instead of staring at their differences.
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