This article is a quick overview of the state-of-the-art academic research in wearable sensors as applied to communicating emotion and affection and is based on a talk by Rosalind Picard from MIT Labs at Radcliffe Institute (reference at the end). Although the article is brief, number of references below is significant if you are interested to dig deeper. I highly recommend to watch Rosalind’s talk, it is technical, engaging, and inspiring.
We can extrapolate emotional state of a person based on skin conductance response. In the research, experimenters wore sensors on both wrists and ankles. Surprising finding was that response significantly varies between sensors on different parts of the body. Skin conductance could be simplistically thought of as body reaction to stress or stimuli. So the spikes in the image below are the most stressful moments measured through the day.
The skin conductance response, also known as the electrodermal response (and in older terminology as “galvanic skin response”), is the phenomenon that the skin momentarily becomes a better conductor of electricity when either external or internal stimuli occur that are physiologically arousing. Arousal is a broad term referring to overall activation, and is widely considered to be one of the two main dimensions of an emotional response. Measuring arousal is therefore not the same as measuring emotion, but is an important component of it. Arousal has been found to be a strong predictor of attention and memory.
Converting electrical measurements to emotional state is possible because of how our nervous system works. Schematically it is divided into sympathetic, parasympathetic, and enteric systems where emotions in part are determined by responses from the sympathetic system, most active during physical activity and excitement and responsible for increased blood pressure, heart rate, perspiration, and respiration. Together three systems are called Autonomic Nervous System. Both sympathetic and parasympathetic systems innervate most body organs and are important in measurements because skin electrical conductivity spikes could be caused by either. Usually these systems inhibit each other, for example if one accelerates heartbeat, another slows it down. Parasympathetic system is most active during rest and is responsible for digestion, and recovery.
Here’s a chart showing reaction of sympathetic system to stress.
More detailed comparison of sympathetic and parasympathetic systems pointing at how both work in concert inhibiting one another.
To approximate emotional state of a person we can measure skin conductance. This is often used in psychology to quantify reaction to different stimuli and also implemented in lie detectors. Skin Conductance Level (SCL) is a slowly varying value and changes in minutes; Skin Conductance Response (SCR) is fast changing and reflects person’s response where:
- latency tells time of reaction to stimulus,
- rise is time to the peak of conductance,
- amplitude is strength of response,
- and half recovery is how long it takes for the wave to fall back.
There are many devices and sensors already available to extrapolate Electrodermal Activity (EDA) and convert it into interpretation of what is going on inside human’s brain. Sensors can be worn anywhere on fingers, wrists, or ankles.
Size of these EDA sensors is very small.
Packaging EDA into a wristband with the sensors on the inside touching the skin allows for a comfortable design.
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Here’s a wristband by Empatica:
More sensors..
References
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Rosalind W. Picard Radcliffe Institute Mar 14, 2014 Wrist sensors can now collect some of the core physiological data that change with emotion and health. In this talk, Rosalind W. Picard presents examples of new things we can learn from a wristband, including interesting patterns related to sleep, stress, engagement, and epileptic seizures. Rosalind W. Picard is a professor of media arts and sciences, the director of the Affective Computing Research Group, a codirector of the Autism & Communication Technology Initiative, and a codirector of the Things That Think Consortium at the Massachusetts Institute of Technology. Learn more about the people and programs of the Radcliffe Institute at www.radcliffe.harvard.edu. |
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Affective Computing is computing that relates to, arises from, or deliberately influences emotion or other affective phenomena. Emotion is fundamental to human experience, influencing cognition, perception, and everyday tasks such as learning, communication, and even rational decision-making. However, technologists have largely ignored emotion and created an often frustrating experience for people, in part because affect has been misunderstood and hard to measure. Our research develops new technologies and theories that advance basic understanding of affect and its role in human experience. We aim to restore a proper balance between emotion and cognition in the design of technologies for addressing human needs. Our research has contributed to: (1) Designing new ways for people to communicate affective-cognitive states, especially through creation of novel wearable sensors and new machine learning algorithms that jointly analyze multimodal channels of information; (2) Creating new techniques to assess frustration, stress, and mood indirectly, through natural interaction and conversation; (3) Showing how computers can be more emotionally intelligent, especially responding to a person’s frustration in a way that reduces negative feelings; (4) Inventing personal technologies for improving self-awareness of affective state and its selective communication to others; (5) Increasing understanding of how affect influences personal health; and (6) Pioneering studies examining ethical issues in affective computing. Affective Computing research combines engineering and computer science with psychology, cognitive science, neuroscience, sociology, education, psychophysiology, value-centered design, ethics, and more. We bring together individuals with a diversity of technical, artistic, and human abilities in a collaborative spirit to push the boundaries of what can be achieved to improve human affective experience with technology. We encourage you to look at Current and Past Projects for examples of Affective Computing research. Please see this FAQ before emailing us your questions. |
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http://iq.intel.com/human-emotions-next-frontier-in-wearables/?linkId=7801235 Human Emotions, Next Frontier In Wearables In The Future of Wearable Tech, iQ by Intel and PSFK Labs explore the evolving form and function of our Internet-connected devices. This series looks at the rise of wearable technologies and their impact on consumer lifestyles. It’s important for our wearables to do more than just track. Departing from an exclusive focus on individuals to focusing on relationships, which is actually what people spend most of their time thinking about, is a unique opportunity. |
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http://www.slideshare.net/PSFK/psfk-future-of-wearable-technology-report PSFK Future of Wearable Tech Report The Future of Wearable Tech report in collaboration with iQ by intel identifies 10 trends and three major themes that point to the evolving form and function of wearable devices and their influence on the way we live, work and socialize. In our Connected Intimacy theme, we explore how wearables are revolutionizing the way we communicate information about ourselves and maintain relationships over any distance. With the Tailored Ecosystem theme, we look at how these devices are personalizing the world around us and adapting to our ever-changing needs. While the Co-Evolved Possibilities theme considers the potential and promise of a closer union between humans and technology and its impacts on our natural abilities. Within these themes, we take an in-depth look at each of the key trends, bringing them to life with best-in-class examples and connecting the dots with takeaways to help spark thinking and discussion. As you click through the following slides, we hope you find inspiration and innovation that you can leverage and share within your own organization. For more information about the report visit: |
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http://en.wikipedia.org/wiki/Affective_computing Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science.[1]While the origins of the field may be traced as far back as to early philosophical enquiries into emotion,[2] the more modern branch of computer science originated with Rosalind Picard‘s 1995 paper[3] on affective computing.[4][5] A motivation for the research is the ability to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response for those emotions. |
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The Computational Emotion Group at the University of Southern California studies the processes underlying behaviour that people interpret as emotional: · what is the information processing that underlies this behaviour; · how is it physically and mentally manifested; · what features of this behaviour drives an observers interpretation; · how might these interpretations influence the behaviour of an observer and, most importantly, · how might these phenomena be captured and exploited by computational models? From an engineering perspective, we strive to exploit these findings to develop computational systems that communicate more effectively, particularly with respect to “virtual humans” (lifelike-autonomous agents that can participate in multi-model interaction with human user across a variety of educational applications). From a scientific perspective, we strive to use computer generated characters as a tool to investigate how people interpret emotional behaviour and how these interpretations influence (directly or indirectly) memory and decision-making. Major ongoing research efforts include the development of EMA, a computational model of appraisal theory that incorporates a detailed model of appraisal and coping and their influence on cognition, and the Virtual Human project, a long-term interdisciplinary research effort to develop an embodied conversational agent that utilizes EMA to model the cognitive and behavioural influences of emotion. In addition to models of emotion, this project integrates advanced research in natural language processing (including individual research projects on speech recognition, natural language understanding, dialogue management, nonverbal communication, and animation). Several smaller research efforts include: the development of machine learning techniques to characterize expressive behaviours, computation models of social attribution theory and social influence theory, and psychological studies that validate these models. |
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https://sites.google.com/site/memphisemotivecomputing/ https://sites.google.com/site/sidneydmello/ Emotive Computing We are an interdisciplinary research team of psychologists, computer scientists, and cognitive scientists who investigate emotion and cognition. Our research focuses on: · monitoring emotions in learning and problem solving contexts · testing and augmenting theories of emotions and cognition · building computational models of emotional phenomena · developing interfaces that coordinate cognition and complex emotions |
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The Association for the Advancement of Affective Computing The AAAC is a professional, world-wide association for researchers in emotion-oriented/affective computing. The AAAC, formerly the HUMAINE Association, was founded in June 2007, and its first Executive Committee and Management Board were elected in September and October 2007. The history of the process can be viewed in the Association blog. |
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http://www.computer.org/portal/web/tac Computational Modeling of Emotion: Toward Improving the Inter- and Intradisciplinary Exchange The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture |
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http://www.computer.org/cms/Computer.org/transactions/tac/pdfs/tta2013030246.pdf Computational Modeling of Emotion: Toward Improving the Inter- and Intradisciplinary Exchange Rainer Reisenzein, Eva Hudlicka, Mehdi Dastani, Jonathan Gratch, Member, IEEE, Koen Hindriks, Emiliano Lorini, and John-Jules Ch. Meyer Abstract—The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture. |
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http://www.igi-global.com/journal/international-journal-synthetic-emotions-ijse/1144 The International Journal of Synthetic Emotions (IJSE) covers the main issues relevant to the generation, expression, and use of synthetic emotions in agents, robots, systems, and devices. Providing unique, interdisciplinary research from across the globe, this journal covers a wide range of topics such as emotion recognition, sociable robotics, and emotion-based control systems useful to field practitioners, researchers, and academicians. |
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http://thesai.org/Publications/ViewPaper?Volume=1&Issue=6&Code=IJACSA&SerialNo=15 Hybrid Technique for Human Face Emotion Detection Abstract: This paper presents a novel approach for the detection of emotions using the cascading of Mutation Bacteria Foraging optimization and Adaptive Median Filter in highly corrupted noisy environment. The approach involves removal of noise from the image by the combination of MBFO & AMF and then detects local, global and statistical feature form the image. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. Our results so far show the approach to have a promising success rate. An automatic system for the recognition of facial expressions is based on a representation of the expression, learned from a training set of pre-selected meaningful features. However, in reality the noises that may embed into an image document will affect the performance of face recognition algorithms. As a first we investigate the emotionally intelligent computers which can perceive human emotions. In this research paper four emotions namely anger, fear, happiness along with neutral is tested from database in noisy environment of salt and pepper. Very high recognition rate has been achieved for all emotions along with neutral on the training dataset as well as user defined dataset. The proposed method uses cascading of MBFO & AMF for the removal of noise and Neural Networks by which emotions are classified. |
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http://opensmile.sourceforge.net/ openSMILE: The Munich Versatile and Fast Open-Source Audio Feature Extractor. |
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ACII 2013 – Emotion, Technology, Humanities The fifth biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013) will be held in Geneva, Switzerland on September 2-5, 2013, and is organized in cooperation with the AAAI and Technically Co-Sponsored by the IEEE Computer Society. Proceedings will be published by IEEE Computer Society and will be indexed in IEEEXplore. The Conference series on Affective Computing and Intelligent Interaction is the premier international forum for research on affective and multimodal human-machine interaction and systems. This ACII edition will emphasize the humanistic side of affective computing by promoting publications at the cross-road between engineering and human sciences (including biological, social and cultural aspects of human life). The ACII conference will be organized by the Computer Vision and Multimedia Laboratory and the Swiss Center for Affective Sciences from the University of Geneva. Geneva has one of Europe’s most beautiful sceneries, situated between a lake and mountains and is a highly international city where English speaking is common. It has been a cultural center for many centuries and home to many creative spirits in the fields of science and art. The conference will address, but is not limited to, the following topics: You can download the official ACII 2013 Flyer here: ACII2013Flyer.pdf |
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http://www.affective-sciences.org/ NCCR Affective Sciences
The National Center of Competence in Research “Affective Sciences – Emotions in Individual Behaviour and Social Processes” (NCCR Affective Sciences) is one of the first research centres worldwide dedicated to the interdisciplinary study of emotions and their effects on human behaviour and society. More than 100 researchers from various disciplines and universities collaborate in the NCCR Affective Sciences. The NCCR Affective Sciences is funded by the Swiss National Science Foundation (SNSF) and is hosted at the University of Geneva (Swiss Center for Affective Sciences). Research
Many phenomena, ranging from individual cognitive processing to social and collective behavior, cannot be understood without taking into account affective determinants. The NCCR in Affective Sciences brings together disciplines which study the biological, psychological, and social dimensions of affect. The different scientific projects aim to provide a better understanding of affective phenomena (e.g., emotions, motivations, moods, stress, well-being) from various research perspectives and multiple levels of analysis. With its scientists stemming from various backgrounds such as psychology, philosophy, economics, political science, law, criminology, psychiatry, neuroscience, education, sociology, literature, history, and religious and social anthropology, the NCCR places a particular emphasis on the interdisciplinary and integrative collaboration between these different domains of research. |
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http://www.cl.cam.ac.uk/research/rainbow/emotions/ Emotionally intelligent interfaces Andra Adams, Tadas Baltrušaitis, Ntombi Banda, Ian Davies, Vaiva Imbrasaitė, Marwa Mahmoud Erroll Wood & Peter Robinson
With the quick advances in key computing technologies and the heightened user expectation of computers, the development of socially and emotionally adept technologies is becoming a necessity. This project is investigating the inference of people’s mental states from facial expressions, vocal nuances, body posture and gesture, and other physiological signals, and also considering the expression of emotions by robots and cartoon avatars. Facial expressions provide an important spontaneous channel for the communication of both emotional and social displays. They are used to communicate feelings, show empathy, and acknowledge the actions of other people. In this research we investigate how facial expression information can be used as part of a wider context to make useful inferences about a user’s mental state in a natural computing environment, in such a way that increases usability. We draw inspiration from various emotion theories on the role of facial expressions in inferring mental states, most notably the role of temporal and situational context in the process. ApplicationsTesting our inference system has shown the computer to be as accurate as the top 6% of people. But would we want computers that can react to our emotions? Such systems do raise ethical issues: Imagine a computer that could pick the right emotional moment to try to sell you something. There are, however, applications with clear benefits including an emotional hearing aid to assist people with autism, usability testing for software, feedback for on-line teaching, and informing the animation of cartoon figures. We have been working since 2004 on a wearable system that helps people with Autism Spectrum Conditions and Asperger Syndrome, with emotional-social understanding and mind-reading functions. Rana el Kaliouby, who was awarded a PhD for her work on the project, is currently implementing the first prototype of the system at the Massachusetts Institute of Technology’s Media Lab. Metin Sezgin, joined the team in Cambridge to look at ways of improving the inference of mental states by combining multiple sources of information, including biometric sensors. Tal Sobol-Shikler investigated the effects of emotions on non-verbal cues in speech for her PhD at Cambridge, and is now pursuing the work at Ben-Gurion University. Daniel Bernhardt, another research student, extended the system to recognise further channels of affective communication such as posture and gesture. Shazia Afzal investigated applications of affective inference to support on-line teaching systems, and Laurel Riek looking at the expression of emotions by humanoid robots. Another important area is discerning drivers’ mental states. If a driver gets lost while trying to find a route through an unfamiliar city in heavy traffic, the burden of understanding advice from a navigational system could actually be more of a hindrance than a help. We have been working with a major motor manufacturer on systems to detect when a driver is confused, distracted, drowsy or even upset, and adapt the car’s telematic systems accordingly. Six research students are currently working on affective computing: Ian Davies is considering uses in command and control systems, Tadas Baltrušaitis is considering affect in remote communications, Andra Adams is working on affective robotics for autism spectrum conditions, Ntombi Banda is working on fusion techniques for affective inference, Marwa Mahmoud is working on multi-modal inference of occluded gestures, and Vaiva Imbrasaitė is looking at the effects of music on emotions. Further informationPlease follow the links on the left or below for specific projects: § Recreating Darwin’s emotion experiment § Helping children with Autism understand and express emotions Please check the frequently asked questions or contact Peter Robinson for further information. Videos§ Mind-reading machines § Interactive control of music using emotional body expressions § Multi-modal inference for driver-vehicle interaction (2009) § The emotional computer (2010) Press coverage§ BBC report on monitoring car drivers (2008) § Cambridge News (2009) § University press release for The emotional computer (December 2010) § The Telegraph (December 2010) § Athena TV report on affective computing (January 2011, starting about 35 minutes into the video) § Reuters report on affective computing (February 2011) § BBC report (March 2011) § University coverage of the project (March 2011) § Channel Nine Discovery Channel (May 2011, starting about 5 and a half minutes into the main video) § RAI Superquark (June 2011, starting at about 1:07:30 into the main programme) § University coverage of the Darwin project (October 2011) § Reuters report on the Darwin project (October 2011) § BBC World Service programme and report (November 2011) § Anglia news report (March 2012) § New York Times (October 2012) § BBC World Service Click (January 2013) § BBC News article (March 2013) § Blog about a lecture at the Faraday Institute (March 2013) § Dara O Briain’s Science Club and trail (July 2013) § Naked Scientists (September 2013) Other contributors§ Shazia Afzal: Affect inference in learning environments § Daniel Bernhardt: Emotion inference from human body motion § Yujian Gao § Rana el Kaliouby: Mind-reading machines § Tomas Pfister § Laurel Riek: Expression synthesis on robots § Tal Sobol Shikler: Analysis of affective expression in speech |
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http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369 Affective Computing, IEEE Transactions on The IEEE Transactions on Affective Computing is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. |
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Future of Affective Computing Larry Larsen Nov 19, 2013 http://channel9.msdn.com/Series/Microsoft-Research-Luminaries/The-Future-of-Affective-Computing What if a wearable device could tell you when you need to step back from the keyboard and take a break before you respond to an email? Or if you could wear a device that showed you stress levels before you start talking about bills? For those who suffer from hypertension, PTSD, or autism, this information can help apply metrics to an important facet of health. These are just some of the ideas researchers are thinking about in the VIBE group (Visualization and Interaction for Business and Entertainment) within Microsoft Research. AffectAura is a desktop program that streams several inputs from the user to determine an affective state. Another project called MoodWings applies this type of data to a physical talisman, like a butterfly on your wrist. While you may not know something is getting you worked up, your MoodWings butterfly will let you know by increasing wing speed. Mary Czerwinski, Asta Roseway, and Paul Johns show us some of the research projects they are working in the VIBE group.
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http://en.wikipedia.org/wiki/Skin_conductance Skin conductance, also known as galvanic skin response (GSR), electrodermal response (EDR), psychogalvanic reflex (PGR), skin conductance response (SCR) or skin conductance level (SCL), is a method of measuring the electrical conductance of the skin, which varies depending on the moisture of the skin, caused by sweat. Sweat is controlled by the sympathetic nervous system,[1] so skin conductance is used as an indication of psychological or physiological arousal. Therefore, if the sympathetic branch of the autonomic nervous system is highly aroused, then sweat gland activity will also increase, which in turn increases skin conductance. In this way, skin conductance can be used as a measure of emotional and sympathetic responses.[2] There has been a long history of electrodermal activity research, most of it dealing with spontaneous fluctuations or reactions to stimuli. |
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http://www.media.mit.edu/galvactivator/faq.html The skin conductance response, also known as the electrodermal response (and in older terminology as “galvanic skin response”), is the phenomenon that the skin momentarily becomes a better conductor of electricity when either external or internal stimuli occur that are physiologically arousing. Arousal is a broad term referring to overall activation, and is widely considered to be one of the two main dimensions of an emotional response. Measuring arousal is therefore not the same as measuring emotion, but is an important component of it. Arousal has been found to be a strong predictor of attention and memory. |
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http://www.columbia.edu/~bf2151/figner_murphy.pdf Using skin conductance in judgment and decision making research Figner, B., & Murphy, R. O. (in press). Using skin conductance in judgment and decision making research. In M. Schulte-Mecklenbeck, A. Kuehberger, & R. Ranyard (Eds.), A handbook of process tracing methods for decision research. New York, NY: Psychology Press. |
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http://www.qsensortech.com/resources/understanding-eda/tonic-phasic-changes/ Skin conductance measurement is traditionally characterized into two types – tonic and phasic – which can roughly be thought of as “the smooth underlying slowly-changing levels” vs. “the rapidly changing peaks.” Tonic – Tonic skin conductance is generally considered to be the level of skin conductance in the absence of any particular discrete environmental event or external stimuli. This slow-changing level is generally referred to as Skin Conductance Level (SCL). Tonic skin conductance level can slowly vary over time in an individual depending upon his or her psychological state, hydration, skin dryness, and autonomic regulation. Tonic changes in the skin conductance level typically occur in a period of from tens of seconds to minutes. Phasic – Phasic skin conductance measurements are typically associated with short-term events and occur in the presence of discrete environmental stimuli (sight, sound, smell, cognitive processes that precede an event such as anticipation, decision making, etc). Phasic changes usually show up as abrupt increases in the skin conductance, or “peaks” in the skin conductance. These peaks are generally referred to as Skin Conductance Responses (SCRs). |
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Social Psychology Principles http://2012books.lardbucket.org/books/social-psychology-principles/index.html Charles Stangor |
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Fight, Flight or Freeze Response to Stress http://www.stressstop.com/stress-tips/articles/fight-flight-or-freeze-response-to-stress.php |
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Four Fs (evolution) http://en.wikipedia.org/wiki/Four_Fs_(evolution) In evolutionary biology, people often speak of the four Fs which are said to be the four basic drives or mind states that animals (including humans) are evolutionarily adapted to be good at: fighting, fleeing, feeding, and reproduction. Some replace ‘reproduction’ with ‘fornication’ or ‘fe-mating’ or the crass ‘fucking’. The term is also used in reference to the function of the hypothalamus in the brain, which regulates the four f’s primarily through endocrine releasing hormones. |
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Autonomic and Motor Nervous System Innervates all effector organs and tissues except for skeletal muscles. It is autonomic because it functions subconsciously and involuntarily. |
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Sympathetic nervous system http://en.wikipedia.org/wiki/Sympathetic_nervous_system The (ortho-) sympathetic nervous system (SNS) is one of three major parts of the autonomic nervous system (the others being the enteric and parasympathetic systems). Its general action is to mobilize the body’s nervous system fight-or-flight response. It is, however, constantly active at a basic level to maintain homeostasis. The name of the system has its origin related with the concept of sympathy. |
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http://www.sciencedaily.com/articles/s/sympathetic_nervous_system.htm Sympathetic nervous system The sympathetic nervous system (SNS) is part of the autonomic nervous system (ANS), which also includes the parasympathetic nervous system (PNS). The sympathetic nervous system activates what is often termed the fight or flight response. Like other parts of the nervous system, the sympathetic nervous system operates through a series of interconnected neurons. Sympathetic neurons are frequently considered part of the peripheral nervous system (PNS), although there are many that lie within the central nervous system (CNS). |
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Emotional Intelligence – Recognize basic emotions Uploaded on Jul 10, 2007 Trough human evolution we have learned to recognize basic emotional states by reading facial expression. See how good you are in this movie clip. |
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Course | Human Behavioral Biology by StanfordUniversity 25 videos |
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The Physical Effects of Stress How the Body Reacts http://www.stress-management-4-women.com/physical-effects-of-stress.html The main part of the brain handling the emotions of stress is known as the limbic system. It is set into motion when you perceive a threat. The network of endocrine glands begin to react. The adrenal gland, found atop the kidneys releases the hormone, cortisol, which makes its way to the brain through the bloodstream. Adrenalin, dopamine, noradrenalin, and endorphins also are released into your bloodstream, producing a wide variety of effects. These hormones increase your energy levels, speed up your heart rate, accelerate your blood flow, heighten your brain activity, produce rapid breathing, and slow down digestion. All of your energy reserves are shifted from regular body functions to functions that will help you survive the stressful situation. |
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Overview of the Autonomic Nervous System |
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Autonomic Nervous System & Fertility http://blog.shadygrovefertility.com/2013/09/06/autonomic-nervous-system-fertility/ |
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Parasympathetic nervous system http://en.wikipedia.org/wiki/Parasympathetic_nervous_system The parasympathetic nervous system (PSNS) is one of three main divisions of the autonomic nervous system (ANS), the other two being the sympathetic and enteric systems. The ANS is responsible for regulation of internal organs and glands, which occurs unconsciously. To be specific, the parasympathetic system is responsible for stimulation of “rest-and-digest” or “feed and breed” activities that occur when the body is at rest, especially after eating, including sexual arousal, salivation, lacrimation (tears), urination, digestion and defecation. Its action is described as being complementary to that of one of the other main branches of the ANS, the sympathetic nervous system, which is responsible for stimulating activities associated with the fight-or-flight response. |
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Towards long term monitoring of electrodermal activity in daily life Cornelia Kappeler-Setz, Franz Gravenhorst, Johannes Schumm, Bert Arnrich, Gerhard Tro¨ster 19 October 2011 Springer-Verlag London Limited 2011 Abstract Manic depression, also known as bipolar disorder, is a common and severe form of mental disorder. The European research project MONARCA aims at developing and validating mobile technologies for multiparametric, long term monitoring of physiological and behavioral information relevant to bipolar disorder. One aspect of MONARCA is to investigate the long term monitoring of Electrodermal activity (EDA) to support the diagnosis and treatment of bipolar disorder patients. |
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A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity Swenson, N.C., and R.W. Picard, with Ming-Zher Poh. “A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity.” Biomedical Engineering, IEEE Transactions On 57.5 (2010) : 1243-1252. Copyright © 2010, IEEE http://dx.doi.org/10.1109/tbme.2009.2038487 http://dspace.mit.edu/handle/1721.1/62149 Electrodermal activity (EDA) is a sensitive index of sympathetic nervous system activity. Due to the lack of sensors that can be worn comfortably during normal daily activity and over extensive periods of time, research in this area is limited to laboratory settings or artificial clinical environments. We developed a novel, unobtrusive, nonstigmatizing, wrist-worn integrated sensor, and present, for the very first time, a demonstration of long-term, continuous assessment of EDA outside of a laboratory setting. We evaluated the performance of our device against a Food and Drug Administration (FDA) approved system for the measurement of EDA during physical, cognitive, as well as emotional stressors at both palmar and distal forearm sites, and found high correlations across all the tests. We also evaluated the choice of electrode material by comparing conductive fabric with Ag/AgCl electrodes and discuss the limitations found. An important result presented in this paper is evidence that the distal forearm is a viable alternative to the traditional palmar sites for EDA measurements. Our device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA. This paper opens up opportunities for future investigations that were previously not feasible, and could have far-reaching implications for diagnosis and understanding of psychological or neurological conditions. |
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What is Electrodermal Activity (EDA)? http://www.qsensortech.com/resources/understanding-eda/what-is-eda/ Electrodermal activity refers to electrical changes measured at the surface of the skin that arise when the skin receives innervating signals from the brain. For most people, if you experience emotional arousal, increased cognitive workload or physical exertion, your brain sends signals to the skin to increase the level of sweating. You may not feel any sweat on the surface of the skin, but the electrical conductance increases in a measurably significant way as the pores begin to fill below the surface. |
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https://courses.cit.cornell.edu/courses/ee476/FinalProjects/s2006/hmm32_pjw32/index.html EDA Sensor Project Skin conductance is known to be correlated with an individual’s emotional state. It is often used in psychology for quantifying a person’s reaction to different stimulus. It is also often implemented in a traditional lie detectors. The current theory is that a person under arousal generates sweat at the skin that changes the skin’s electrical properties. However, this explanation is still debatable, as some scientists claim that the conductance changes too fast to attribute the cause to sweat. Because a change in mental state can be observed by the change in skin conductance, the arousal level of a person can be quantified. While one of us is interested in visualizing the meditative experiences, the other wanted to recreate the lie detector test seen in one too many movies. We were also intrigued by this project because of the controversial results found in different studies such as the ‘prestimulus’ response, and the characterization of psychotics using skin conductance patterns. There was also the allure of the use of skin conductance in many parapsychological experiments. |
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http://www.ncbi.nlm.nih.gov/pubmed/20172811 A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. Poh MZ1, Swenson NC, Picard RW. Abstract Electrodermal activity (EDA) is a sensitive index of sympathetic nervous system activity. Due to the lack of sensors that can be worn comfortably during normal daily activity and over extensive periods of time, research in this area is limited to laboratory settings or artificial clinical environments. We developed a novel, unobtrusive, nonstigmatizing, wrist-worn integrated sensor, and present, for the very first time, a demonstration of long-term, continuous assessment of EDA outside of a laboratory setting. We evaluated the performance of our device against a Food and Drug Administration (FDA) approved system for the measurement of EDA during physical, cognitive, as well as emotional stressors at both palmar and distal forearm sites, and found high correlations across all the tests. We also evaluated the choice of electrode material by comparing conductive fabric with Ag/AgCl electrodes and discuss the limitations found. An important result presented in this paper is evidence that the distal forearm is a viable alternative to the traditional palmar sites for EDA measurements. Our device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA. This paper opens up opportunities for future investigations that were previously not feasible, and could have far-reaching implications for diagnosis and understanding of psychological or neurological conditions. |
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http://www.ncbi.nlm.nih.gov/pubmed/20070159 Wearable sensor glove based on conducting fabric using electrodermal activity and pulse-wave sensors for e-health application. Lee Y1, Lee B, Lee M. Abstract Improvement of the quality and efficiency of health in medicine, both at home and the hospital, calls for improved sensors that might be included in a common carrier such as a wearable sensor device to measure various biosignals and provide healthcare services that use e-health technology. Designed to be user-friendly, smart clothes and gloves respond well to the end users for health monitoring. This study describes a wearable sensor glove that is equipped with an electrodermal activity (EDA) sensor, pulse-wave sensor, conducting fabric, and an embedded system. The EDA sensor utilizes the relationship between drowsiness and the EDA signal. The EDA sensors were made using a conducting fabric instead of silver chloride electrodes, as a more practical and practically wearable device. The pulse-wave sensor measurement system, which is widely applied in oriental medicinal practices, is also a strong element in e-health monitoring systems. The EDA and pulse-wave signal acquisition module was constructed by connecting the sensor to the glove via a conductive fabric. The signal acquisition module is then connected to a personal computer that displays the results of the EDA and pulse-wave signal processing analysis and gives accurate feedback to the user. This system is designed for a number of applications for the e-health services, including drowsiness detection and oriental medicine. |
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EDA Electrodermal Activity Amplifier – EDA100C http://www.biopac.com/gsr-eda-galvanic-skin-response-amplifier-electrodermal-activity The EDA100C measures both the skin conductance level (SCL) and skin conductance response (SCR) as they vary with sweat gland (eccrine) activity due to stress, arousal or emotional excitement. The EDA100C uses a constant voltage (0.5 V) technique to measure skin conductance. The controls allow selection of absolute (SCL+SCR) or relative (SCR) skin conductance measurements. For wireless Electrodermal Activity recording, see the BioNomadix BN-PPGED amplifier (dual-channel transmitter/receiver set for PPG and/or EDA).
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The measurement of arousal by the means of electrodermal activity during an actually performed balance beam routine and observational learning of the same routine Knufinke, Melanie (2012) The measurement of arousal by the means of electrodermal activity during an actually performed balance beam routine and observational learning of the same routine. Abstract: The aim of this study was to gain insight into arousal patterns of gymnasts during actual performance (AP) and observational learning (OL) of a beam routine. Arousal was measured by the means of electro dermal activity (EDA) with a wireless, logging sensor. A second aim was to get insight in EDA recordings obtained from different anatomical sensor locations (wrist, forearm, palm). EDA was measured three times when five regional level gymnasts were performing their beam routines, as well as during OL of these routines. EDA was recorded from the wrist and forearm. During the last OL condition, EDA was recorded on the palm. A control group was formed of 11 non-gymnasts to examine whether arousal patterns are due to the stimulus or other factors. Non-gymnasts participated in the OL condition as well when EDA was measured at the palm. Women artistic gymnastics (WAG) employ short intervals of high skilled and demanding exercises eliciting varying levels of arousal. Arousal regulation has become important to enhance performance and prevent injury in WAG and OL is often used to regulate arousal. Comparison of arousal patterns during OL and AP revealed higher skin conductance responses (SCR) during AP. Sensors attached to the forearm during AP had less motion artifacts and showed comparable EDA sensitivity to the sensor attached to the wrist. During OL, the palm has shown to be the most sensitive location when measuring EDA. Although correlations between the conditions and locations were low, some similar trends have been found in EDA waveform. More appropriate statistical methods are needed to further analyze arousal in both conditions. Recommendations for further research are outlined in the discussion. This study gave some insight into arousal patterns during AP an OL and has significant importance in fields as sport psychology, psychophysiology as well as sport physiology. |
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Empatica E3 Wristband The smallest bracelet to monitor physiological signals in real time.
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Alan Tatourian 