Stress level assessment with non-intrusive sensors

Mención Internacional en el título de doctorStress is an involuntary reaction where the human body changes from a calm state to an excited state in order to preserve the integrity of the organism. Small amount of stress should be good to became entrepreneur and learn new ways of thinking, but continuous stress can carry an array of daily risks, such as, cardiovascular diseases, hair loss, diabetes or immune dysregulation. Recognize how, when and where it occurs has become a step in stress assessment. Stress recognition starts from 1973 until now. This disease has become a problem in recent years because has increased the number of cases, especially in workers where his/her performance decreases. Stress reactions are provoked for the Autonomous Nervous System (ANS) and one way to estimate it could be found in physiological signals. A list of a variety wearable sensor is presented to capture these reactions, trying to minimize the risk of distraction due to external factors. The aim of this work thesis is to detect stress for level assessment. A combination of different physiological signals is selected to extract stress feature an classify in a rating scale from relax to breakdown situations. This thesis proposes a new feature extraction model to understand physiological Galvanic Skin Response (GSR) reactions. Last methods conclude in incongruent results that are not interpretable. This model propose a robust algorithm that can be used in real-time (low time computability) and results are sparse in time to obtain an easily statistical and graphical interpretation. Signal processing methods of heart rhythm and hormone cortisol are included to develop a robust feature extraction method of stress reactions. A combination of electrodermal, heart and hormone analysis is presented to know in real-time the state of the individual. These features have been selected because the acquisition is non-intrusive avoiding other factor such as distractions. This thesis is application-focused and highly multidisciplinary. A complete feature extraction model is presented including the new electrodermal model named and usual heart rhythm techniques. Three experiments were evaluated: a) a feature selection model using neurocognitive games, b) a stress classifier in time during public talks, and c) a real-time stress assessment classifier in a five-star rating scale. This thesis improve stress detection overcoming a system to capture physiological responses, analyze and conclude a stress assessment decision. We discussed past state of the art and propose a new method of feature extraction using signal processing improvements. Three different scenarios were evaluated to confirm the achievement of aims proposed.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Luis Ignacio Santamaría Caballero.- Vocal: Mª Isabel Valera Martíne

Plug-in to fear: game biosensors and negative physiological responses to music

The games industry is beginning to embark on an ambitious journey into the world of biometric gaming in search of more exciting and immersive gaming experiences. Whether or not biometric game technologies hold the key to unlock the “ultimate gaming experience” hinges not only on technological advancements alone but also on the game industry’s understanding of physiological responses to stimuli of different kinds, and its ability to interpret physiological data in terms of indicative meaning. With reference to horror genre games and music in particular, this article reviews some of the scientific literature relating to specific physiological responses induced by “fearful” or “unpleasant” musical stimuli, and considers some of the challenges facing the games industry in its quest for the ultimate “plugged-in” experience

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

AI and Automatic Music Generation for Mindfulness

This paper presents an architecture for the creation of emotionally congruent music using machine learning aided sound synthesis. Our system can generate a small corpus of music using Hidden Markov Models; we can label the pieces with emotional tags using data elicited from questionnaires. This produces a corpus of labelled music underpinned by perceptual evaluations. We then analyse participant’s galvanic skin response (GSR) while listening to our generated music pieces and the emotions they describe in a questionnaire conducted after listening. These analyses reveal that there is a direct correlation between the calmness/scariness of a musical piece, the users’ GSR reading and the emotions they describe feeling. From these, we will be able to estimate an emotional state using biofeedback as a control signal for a machine-learning algorithm, which generates new musical structures according to a perceptually informed musical feature similarity model. Our case study suggests various applications including in gaming, automated soundtrack generation, and mindfulness

A preliminary investigation of adult defence style and physiological reactivity to infant distress signals

Species whose offspring require extended care-giving ought to be predisposed to being biologically responsive to their infant\u27s signalling. This paper examined the interplay between biological and psychological aspects of adult response to an infant\u27s distress. HR (heart rate) and GSR (galvanic skin response) were recorded continuously, while 50 adults listened to white noise and an infant cry audio recording. Participants completed the defence style questionnaire and the state trait anxiety inventory. HR acceleration occurred in response to the control sound, while HR decelerated in response to the infant cry. GSR responsiveness was positively correlated with immature and neurotic defence styles. When controlling for other variables, immature defence was a unique and independent predictor of GSR change in response to infant distress. Defence demonstrated a stronger relationship than self-reported anxiety, than that with physiological responsiveness. Employing defence mechanisms appears to reduce an individual\u27s perceived anxiety, though it has little effect on physiological arousal levels

Study, definition and analysis of pilot/system performance measurements for planetary entry experiments

Definition analysis for experimental prediction of pilot performance during planetary entr

Subjective and objective measures

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.info:eu-repo/semantics/acceptedVersio

On the use of AI for generation of functional music to improve mental health

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reduction of cases of dementia in elderly populations, and improvements in markers of general mental well-being such as stress reduction. Here, we describe short case studies addressing general mental well-being (anxiety, stress-reduction) through AI-driven music generation. Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range. However, access to music-making can be prohibitive in terms of access to expertise, materials, and cost. Furthermore the use of existing music for functional outcomes (such as targeted improvement in physical and mental health markers suggested above) can be hindered by issues of repetition and subsequent over-familiarity with existing material. In this paper, we describe machine learning (ML) approaches which create functional music informed by biophysiological measurement across two case studies, with target emotional states at opposing ends of a Cartesian affective space (a dimensional emotion space with points ranging from descriptors from relaxation, to fear). We use Galvanic skin response (GSR) as a marker of psychological arousal and as an estimate of emotional state to be used as a control signal in the training of the ML algorithm. This algorithm creates a non-linear time series of musical features for sound synthesis ‘on-the-fly’, using a perceptually informed musical feature similarity model. We find an interaction between familiarity (or more generally, the featureset model we have implemented) and perceived emotional response so focus on generating new, emotionally-congruent pieces. We also report on subsequent psychometric evaluation of the generated material, and consider how these - and similar techniques -might be useful for a range of functional music generation tasks, for example in nonlinear sound-tracking such as that found in interactive media or video games