Therapy Progress Indicator (TPI): Combining speech parameters and the subjective unit of distress

A posttraumatic stress disorder (PTSD) is a severe handicap in daily life and its treatment is complex. To evaluate the success of treatments, an objective and unobtrusive expert system was envisioned: an therapy progress indicator (TPI). Speech was considered as an excellent candidate for providing an objective, unobtrusive emotion measure. Speech of 26 PTSD patients was recorded while they participated in two reliving sessions: re-experiencing their last panic attack and their last joyful occasion. As a subjective measure, the subjective unit of distress was determined, which enabled the validation of derived speech features. A set of parameters of the speech features: signal, power, zero crossing ratio, and pitch, was found to discriminate between the two sessions. A regression model involving these parameters was able to distinguish between positive and negative distress. This model lays the foundation for an TPI for patients with PTSD, which enables objective and unobtrusive evaluations of therapies

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Physiological signals: The next generation authentication and identification methods!?

Throughout the last 40 years, the security breach caused by human error is often disregarded. To relief the latter problem, this article introduces a new class of biometrics that is founded on processing physiological personal features, as opposed to physical and behavioral features. After an introduction on authentication, physiological signals are discussed, including their advantages, disadvantages, and initial directives for obtaining them. This new class of authentication methods can increase biometrics’ robustness and enables cross validation. I close this article with a brief discussion in which a recap of the article is provided, law, privacy, and ethical issues are discussed, some suggestions for the processing pipeline of this new class of authentication methods are done, and conclusions are drawn

Beyond Biometrics

Throughout the last 40 years, the essence of automated identification of users has remained the same. In this article, a new class of biometrics is proposed that is founded on processing biosignals, as opposed to images. After a brief introduction on biometrics, biosignals are discussed, including their advantages, disadvantages, and guidelines for obtaining them. This new class of biometrics increases biometrics’ robustness and enables cross validation. Next, biosignals’ use is illustrated by two biosignal-based biometrics: voice identification and handwriting recognition. Additionally, the concept of a digital human model is introduced. Last, some issues will be touched upon that will arise when biosignal-based biometrics are brought to practice

Ubiquitous emotion-aware computing

Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people’s emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants’ experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18–20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people’s emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing

Thesis Review Affective Signal Processing (ASP): Unraveling the mystery of emotions, by Egon L. van den Broek

Abstract. The present work is a review of the PhD thesis defended by Egon L. van den Broek on September 16, 2011 at the department of Human Media Interaction, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, Enschede, The Netherlands. I was a member of the PhD dissertation Committee. I was overwhelmed by the quality and the amount of work he did. The thesis is a great contribution to our understanding and harnessing the principles underlying affective multimodal communication and to the development of future real-world technologies for affective signal processing

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

Biosignal Quality Control in Real-World Intelligent Environments

Despite the wealthy of information biosignals cary, with Intelligent Environments (IE) they are often disregarded. We discuss issues we faced with integrating reliable biosignals into a real-world IE. These include the limited conductivity of dry sensors, movement artifacts, and placement issues. Subsequently, we introduce a real-time Signal Quality Indicator (SQI) for ElectroCardioGram (ECG), which consists of a Signal Loss Indicator (SLI) that detects signal capping, flatlining, high-frequency noise, and low-frequency noise. If the SLI detects a signal, the Signal Usability Indicator (SUI) subsequently processes the signal using the reference Pan-Tompkins algorithm and a dedicated filter to extract heart rate. The SQI marks what parts of the signal can and cannot be used for analysis. As such, it allows empirical calibration and, hence, the use of biosensors in real-world IE

Towards Continuous Monitoring of Well-Being

When linked to wearable biosensors, Intelligent Environments could play a pivotal role in continuously monitoring and securing people’s well-being.We explored the value of one such biosensor that records Electrodermal Activity (EDA) by assessing its correlation with participants’ simultaneously, continuously, self-reported arousal. EDA’s frequency and amplitude of ‘non-specific’ SkinConductance Responses in low, mid to high, or high levels of arousal were deter-mined. When participants were in mid/high and high arousal situations, self-reports showed significant correlations (p<.001) with both EDA characteristics. With low arousal, no significant correlations were found. So, in cases of elevated stress, EDA shows the potential of being a reliable signal stress and, hence, also monitor of people’s well-being over time. Follow-up studies should further investigate and validate the utility of EDA monitoring as part of a comprehensive health monitoring strategy and its effectiveness in enhancing well-being