Emotions and Life: Perspectives from Psychology, Biology, and Evolution Robert Plutchik. Washington, DC: American Psychological Association (www.apa.org/books/). 2003, 381 pp., $59.95 (hardcover). Always a mainstay of interest in psychology, in recent years, our field has seen a spike in the study of emotions. Although we are usually able to articulate the whens and whys of our emotional experiences, emotions themselves remain an elusive medium through which we experience and react to our world. Emotions have been long studied, but few have attempted such a review of the topic as has Robert Plutchik in his book entitled Emotions and Life: Perspectives from Psychology, Biology, and Evolution. Plutchik has effectively created a comprehensive text on the history of emotion and society. All other books on the topic of emotions can effectively be considered a footnote to this text. Plutchik offers a lifetime of experience in research and instruction on emotions. This comprehensive text begins by examining popular thought on emotions in humans and nonhuman animals. From there, the author provides a thorough review of the history of emotion research as well as major theories of emotions, and then moves on to the relationship between emotions and thought. At this point Plutchik explores the mechanics involved in the study of emotion including various observational, paper, and self-report measures that are used historically in this pursuit. Plutchik continues by giving commentary on human emotional development in the life span, the evolution of emotions in nature, and how emotions mediate our interaction with the external environment. Following this is a discussion on the role the central nervous system plays in emotions. Finally, Plutchik offers information on the everyday emotions of love and sadness, as well as the role and function of emotional disorders. This text critically reflects Plutchik's extensive history on the study of emotion through content, flow, and his exhaustive review of the subject matter. This book utilizes the formatting of a textbook, including testimonials, first-person examples, and detailed charts and figures in order to illustrate the theme and information of each section. This text reads, however, like a narrative in its language and flow of information. These two qualities make this text appropriate for classroom instruction as well as a personal reference for researchers and practitioners. …

Emotions and Life: Perspectives from Psychology, Biology, and Evolution

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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Photoplethysmogram Analysis and Applications: An Integrative Review

During the past few years, I have become interested in evidence-based software engineering [5]. The evidence-based paradigm began in medicine and has totally revolutionised medical research [7]. It is now being adopted by a number of humancentered disciplines such as nursing, social policy, education etc. Although I have some reservations about whether software engineering can adopt fully the evidencebased paradigm, I am convinced that we should adopt certain aspects of the approach immediately. In particular, I believe we should adopt systematic review in place of ad hoc literature reviews, and recognise that a systematic review is a research method in its own right. Currently, all PhD students need to conduct a literature review as a part of their research but such reviews are seldom performed with the rigour now being required in other disciplines (including Information Systems Research, [6]). A systematic review is rigorous method of synthesising existing research related to a topic of interest. Quantitative meta-analysis is one form of systematic review, but it is not mandatory to provide a quantitative summary. Systematic reviews aim to provide a synthesis that is complete (with respect to defined criteria) and unbiased. Systematic reviews must be rigorous i.e. be conducted according to a well-defined procedure and open i.e. the research process must be reported in a manner is understandable to other researchers.

Systematic reviews

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.

Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

Consumer Wearables and Affective Computing for Wellbeing Support

Wearables like smartwatches or wrist bands equipped with pervasive sensors enable us to monitor our physiological signals. In this study, we address the question whether they can help us to recognize our emotions in our everyday life for ubiquitous computing. Using the systematic literature review, we identified crucial research steps and discussed the main limitations and problems in the domain.

Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress

Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals. In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity, especially in field studies. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung, Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation.

Review of Consumer Wearables in Emotion, Stress, Meditation, Sleep, and Activity Detection and Analysis.

Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

https://dl.acm.org/doi/pdf/10.1145/3448891.3450332

Adam Dziadek

Papers

Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress

Consumer Wearables and Affective Computing for Wellbeing Support

Review of Consumer Wearables in Emotion, Stress, Meditation, Sleep, and Activity Detection and Analysis.

Consumer Wearables and Affective Computing for Wellbeing Support