Global ETD Search

1	Stress Detection for Keystroke Dynamics Lau, Shing-hon 01 May 2018 (has links) Background. Stress can profoundly affect human behavior. Critical-infrastructure operators (e.g., at nuclear power plants) may make more errors when overstressed; malicious insiders may experience stress while engaging in rogue behavior; and chronic stress has deleterious effects on mental and physical health. If stress could be detected unobtrusively, without requiring special equipment, remedies to these situations could be undertaken. In this study a common computer keyboard and everyday typing are the primary instruments for detecting stress. Aim. The goal of this dissertation is to detect stress via keystroke dynamics – the analysis of a user’s typing rhythms – and to detect the changes to those rhythms concomitant with stress. Additionally, we pinpoint markers for stress (e.g., a 10% increase in typing speed), analogous to the antigens used as markers for blood type. We seek markers that are universal across all typists, as well as markers that apply only to groups or clusters of typists, or even only to individual typists. Data. Five types of data were collected from 116 subjects: (1) demographic data, which can reveal factors (e.g., gender) that influence subjects’ reactions to stress; (2) psychological data, which capture a subject’s general susceptibility to stress and anxiety, as well as his/her current stress state; (3) physiological data (e.g., heart-rate variability and blood pressure) that permit an objective and independent assessment of a subject’s stress level; (4) self-report data, consisting of subjective self-reports regarding the subject’s stress, anxiety, and workload levels; and (5) typing data from subjects, in both neutral and stressed states, measured in terms of keystroke timings – hold and latency times – and typographical errors. Differences in typing rhythms between neutral and stressed states were examined to seek specific markers for stress. Method. An ABA, single-subject design was used, in which subjects act as their own controls. Each subject provided 80 typing samples in each of three conditions: (A) baseline/neutral, (B) induced stress, and (A) post-stress return/recovery-to-baseline. Physiological measures were analyzed to ascertain the subject’s stress level when providing each sample. Typing data were analyzed, using a variety of statistical and machine learning techniques, to elucidate markers of stress. Clustering techniques (e.g., K-means) were also employed to detect groups of users whose responses to stress are similar. Results. Our stressor paradigm was effective for all 116 subjects, as confirmed through analysis of physiological and self-report data. We were able to identify markers for stress within each subject; i.e., we can discriminate between neutral and stressed typing when examining any subject individually. However, despite our best attempts, and the use of state-of-the-art machine learning techniques, we were not able to identify universal markers for stress, across subjects, nor were we able to identify clusters of subjects whose stress responses were similar. Subjects’ stress responses, in typing data, appear to be highly individualized. Consequently, effective deployment in a realworld environment may require an approach similar to that taken in personalized medicine. Keystroke dynamics keystroke biometrics behavioral biometrics stress stress detection affect detection
2	Dataset quality assessment through camera analysis : Predicting deviations in plant production Sadashiv, Aravind January 2022 (has links) Different type of images provided by various combinations of cameras have the power to help increase and optimize plant growth. Along with a powerful deep learning model, for the purpose of detecting these stress indicators in RGB images, can significantly increase the harvest yield. The field of AI solutions in agriculture is not vastly explored and this thesis aims to take a first step in helping explore different techniques to detect early plant stress. Within this work, different types and combinations of camera modules will initially be reviewed and evaluated based on the amount of information they provide. Using the chosen cameras, we manually set up datasets and annotations, chose and then trained a suitable and appropriate algorithm to predict deviations from an ideal state in plant production. The algorithm chosen was Faster RCNN, which resulted in having a very high detection accuracy. Along with the main type of cameras, a new particular type of images analysis, named SI-NDVI, is done using a particular combination of the main three cameras and the results show that it is able to detect vegetation and able to predict or show if a plant is stressed or not. An in-depth research is done on all these techniques to create a good quality dataset for the purpose of early stress detection. / Olika typer av bilder försedda av olika kombinationer av kameror har kapaciteten att hjälpa öka och optimera odling av växter. Tillsammans med en kraftfull deep learning-modell, för att detektera olika stressindikatorer i RGB bilder, kan signifikant öka skördar. Fältet av AI-lösningar inom jordbruk är inte väl utforskat och denna uppsats siktar på att ta ett första steg i utforskandet av olika tekniker för att detektera tidig stress hos växter. Inom detta arbete kommer olika typer och kombinationer av kameramoduler bli utvärderade baserat på hur mycket information de kan förse. Genom att använda de valda kamerorna skapar vi själva dataseten och kategoriserar dem, därefter välja och träna en lämplig algoritm för att förutspå förändringar från ett idealt tillstånd för växtens tillväxt. Algoritmen som valdes var Faster RCNN, vilken hade en väldigt hög träffsäkerhet. Parallellt med de huvudsakliga kamerorna genomförs en ny typ av bildanalys vid namn SI-NDVI genom användandet av en särskild kombination av de tre kameror och resultat visar att det är möjligt att detektera vegetation och förutspå eller visa om en växt är stressad eller inte. En fördjupad undersökning genomförs på alla dessa tekniker för att skapa ett dataset av god kvalité för att kunna förutspå tidig stress. Deep Learning Faster RCNN SI-NDVI Thermal Imaging Stress detection Computer and Information Sciences Data- och informationsvetenskap
3	Technological solution for the identification and reduction of stress level using wearables Raymondi, Luis Guillermo Antezana, Guzman, Fabricio Eduardo Aguirre, Armas-Aguirre, Jimmy, Agonzalez, Paola 01 June 2020 (has links) El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this article, a technological solution is proposed to identify and reduce the level of mental stress of a person through a wearable device. The proposal identifies a physiological variable: Heart rate, through the integration between a wearable and a mobile application through text recognition using the back camera of a smartphone. As part of the process, the technological solution shows a list of guidelines depending on the level of stress obtained in a given time. Once completed, it can be measured again in order to confirm the evolution of your stress level. This proposal allows the patient to keep his stress level under control in an effective and accessible way in real time. The proposal consists of four phases: 1. Collection of parameters through the wearable; 2. Data reception by the mobile application; 3. Data storage in a cloud environment and 4. Data collection and processing; this last phase is divided into 4 sub-phases: 4.1. Stress level analysis, 4.2. Recommendations to decrease the level obtained, 4.3. Comparison between measurements and 4.4. Measurement history per day. The proposal was validated in a workplace with people from 20 to 35 years old located in Lima, Peru. Preliminary results showed that 80% of patients managed to reduce their stress level with the proposed solution. / Revisión por pares Cloud Technology Heart Rate Mobile Health Application Real-time Text Recognition Stress Detection Technology Wearable
4	An Investigation of Thermal Imaging to Detect Physiological Indicators of Stress in Humans Cross, Carl Brady 25 May 2013 (has links) No description available. Biomedical Engineering thermal imaging stress detection physiological monitoring stand-off detection image processing
5	IoT DEVELOPMENT FOR HEALTHY INDEPENDENT LIVING Greene, Shalom 01 January 2017 (has links) The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management. Internet of Things smart healthcare quality of life affective computing stress detection voice automation Amazon Echo biosensing GSR automated facial expression analysis Computer Engineering Electrical and Computer Engineering
6	IoMT-Based Accurate Stress Monitoring for Smart Healthcare Rachakonda, Laavanya 05 1900 (has links) This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. This research proposes a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects in the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments, with iLog Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. SaYoPillow has 96% accuracy which is close to other existing research works. This research can not only help in keeping an individual self-aware by providing immediate feedback to change the lifestyle of the person in order to lead a healthier life, but can also play a significant role in the state-of-the-art by allowing computing on the edge devices. Computer Science Health Sciences, Mental Health
7	Détection multimodale du stress pour la conception de logiciels de remédiation / Multimodal stress detection for remediation software design Soury, Mariette 28 October 2014 (has links) Ces travaux de thèse portent sur la reconnaissance automatique du stress chez des humains en interaction dans des situations anxiogènes: prise de parole en public, entretiens et jeux sérieux à partir d'indices audio et visuels.Afin de concevoir des modèles de reconnaissance automatique du stress, nous utilisons : des indices audio calculés à partir de la voix des sujets, capturée par un micro cravate; et des indices visuels calculés soit à partir de l'expression faciale des sujets capturés par une webcam, soit à partir de la posture des sujets capturée par une Kinect. Une partie des travaux portent sur la fusion des informations apportées par les différentes modalités.L'expression et la gestion du stress sont influencées à la fois par des différences interpersonnelles (traits de personnalité, expériences passées, milieu culturel) et contextuelles (type de stresseur, enjeux de la situation). Nous évaluons le stress sur différents publics à travers des corpus de données collectés pendant la thèse: un public sociophobe en situation anxiogène, face à une machine et face à des humains; un public non pathologique en simulation d'entretien d'embauche; et un public non pathologique en interaction face à un ordinateur ou face au robot humanoïde Nao. Les comparaisons inter- individus, et inter-corpus révèlent la diversité de l'expression du stress.Une application de ces travaux pourrait être la conception d'outils thérapeutiques pour la maitrise du stress, notamment à destination des populations phobiques.Mots clé : stress, phobie sociale, détection multimodale du stress , indices audio du stress, indices faciaux du stress, indices posturaux du stress, fusion multimodale / This thesis focuses on the automatic recognition of human stress during stress-inducing interactions (public speaking, job interview and serious games), using audio and visual cues.In order to build automatic stress recognition models, we used audio cues computed from subjects' voice captured via a lapel microphone, and visual cues computed either form subjects' facial expressions captured via a webcam, or subjects' posture captured via a Kinect. Part of this work is dedicated to the study of information fusion form those various modalities.Stress expression and coping are influenced both by interpersonal differences (personality traits, past experiences, cultural background) and contextual differences (type of stressor, situation's stakes). We evaluated stress in various populations in data corpora collected during this thesis: social phobics in anxiety-inducing situations in interaction with a machine and with humans; apathologic subjects in a mock job interview; and apathologic subjects interaction with a computer and with the humanoid robot Nao. Inter-individual and inter-corpora comparisons highlight the variability of stress expression.A possible application of this work could be the elaboration of therapeutic software to learn stress coping strategies, particularly for social phobics.Key words: stress, social phobia, multimodal stress detection, stress audio cues, stress facial cues, stress postural cues, multimodal fusion Stress Phobie sociale Détection multimodale du stress Indices audio du stress Indices faciaux du stress Indices posturaux du stress Fusion multimodale Stress Social phobia Multimodal stress detection Stress audio cues Stress facial cues Stress postural cues Multimodal fusion
8	Enhanching the Human-Team Awareness of a Robot Wåhlin, Peter January 2012 (has links) The use of autonomous robots in our society is increasing every day and a robot is no longer seen as a tool but as a team member. The robots are now working side by side with us and provide assistance during dangerous operations where humans otherwise are at risk. This development has in turn increased the need of robots with more human-awareness. Therefore, this master thesis aims at contributing to the enhancement of human-aware robotics. Specifically, we are investigating the possibilities of equipping autonomous robots with the capability of assessing and detecting activities in human teams. This capability could, for instance, be used in the robot's reasoning and planning components to create better plans that ultimately would result in improved human-robot teamwork performance. we propose to improve existing teamwork activity recognizers by adding intangible features, such as stress, motivation and focus, originating from human behavior models. Hidden markov models have earlier been proven very efficient for activity recognition and have therefore been utilized in this work as a method for classification of behaviors. In order for a robot to provide effective assistance to a human team it must not only consider spatio-temporal parameters for team members but also the psychological.To assess psychological parameters this master thesis suggests to use the body signals of team members. Body signals such as heart rate and skin conductance. Combined with the body signals we investigate the possibility of using System Dynamics models to interpret the current psychological states of the human team members, thus enhancing the human-awareness of a robot. / Användningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot. / <p>The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.</p> robotics Artificial Intelligence AI System Dynamics Wireless Body Area Network WBAN sensors Psychology physiology human behavior Human-Robot-Interaction HRI robot Human-Team awareness human-robot RapidMiner Vensim JaHMM hidden markov models HMM eclipse rcp ARFF recognizer classification cross-validation human behavior stress detection Galvanic Skin Response Heart Rate Skin Temperature Finger Temperature Electrocardiogram ECG GSR ST Pupil Diameter PD Patient monitoring GPS Data acquisition dataset data set MODERE Yerkes-Dodson SD model SD Confusion matrix spatial MOUT Java Agent robotic agent Machine Learning activity recognition team oriented robot Evaluation Decoding Learning Forward Algorithm Backward Algorithm Baum-Welch feedback loop Situation Awareness SA Shared Mental Models SMM

Search results