scholarly journals Predicting Stress in Teens from Wearable Device Data Using Machine Learning Methods

2020 ◽  
Author(s):  
Claire W. Jin ◽  
Ame Osotsi ◽  
Zita Oravecz
Keyword(s):  
High Stress ◽  
Well Being ◽  
Physiological Signals ◽  
Support Vector ◽  
Stressful Event ◽  
Emotional States ◽  
Machine Model ◽  
Daily Lives ◽  
Physiological Indicators ◽  
Physiological Markers

AbstractStress management is a pervasive issue in the modern high schooler’s life. Despite many efforts to support adolescents’ mental well-being, teenagers often fail to recognize signs of high stress and anxiety until their emotions have escalated. Being able to identify early signs of these intense emotional states and predict their onset using physiological signals collected passively in real-time could help teenagers improve their awareness of their emotional well-being and take a more proactive approach to managing their emotions. To evaluate the potential of this approach, we collected data from high schoolers with Empatica E4 wearable health monitors (wristband) while they were living their daily lives. The data consisted of stressful event reports and physiological markers over the course of 4 weeks. We developed a random forest model and a support vector machine model and systematically assessed their performance in terms of predicting the onset of stress events and identifying physiological signals of stress. The models showed strong performance in terms of these measures and provided insights on physiological indicators of adolescent stress.

scholarly journals Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 511 ◽  
Author(s):  
Lizheng Pan ◽  
Zeming Yin ◽  
Shigang She ◽  
Aiguo Song
Keyword(s):  
Decision Making ◽  
Emotion Recognition ◽  
Physiological Signals ◽  
Support Vector ◽  
Emotional States ◽  
Multiple Perspectives ◽  
Team Collaboration ◽  
Advantages And Disadvantages ◽  
Nonlinear Features ◽  
Identification Strategy

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.

scholarly journals Personalized Characterization of Emotional States in Patients with Bipolar Disorder

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1174
Author(s):  
Pavel Llamocca ◽  
Victoria López ◽  
Matilde Santos ◽  
Milena Čukić
Keyword(s):  
Random Forest ◽  
Relevant Information ◽  
Mental Illnesses ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Emotional States ◽  
Physiological Indicators ◽  
Medical Observation ◽  
Relevant Variables ◽  
Available Information

There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental illnesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depression, in this work, supervised machine learning (ML) algorithms were applied to classify the different states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and medical observation at regular appointments. The variables were processed and then statistical techniques of data analysis, normalization, noise reduction, and feature selection were applied. An individual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regression, and Support Vector Machine algorithms were applied with different configurations. The results allowed us to draw some conclusions. Random Forest achieved the most accurate classification, but none of the applied models were the best technique for all patients. Besides, the classification using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smartwatch was the most relevant source of information.

scholarly journals Prediction of Daily Happiness Using Supervised Learning of Multimodal Lifelog Data

2019 ◽  
pp. 145-152
Author(s):  
Tetsuya Yamamoto ◽  
Junichiro Yoshimoto ◽  
Eric Murillo-Rodriguez ◽  
Sergio Machado
Keyword(s):  
Prediction Accuracy ◽  
Positive Mood ◽  
Well Being ◽  
Sleep Time ◽  
Support Vector ◽  
Negative Events ◽  
Smartphone Apps ◽  
Daily Lives ◽  
Future Studies ◽  
Novel Approach

Developing an approach to predict happiness based on individual conditions and actions could enable us to select daily behaviors for enhancing well-being in life. Therefore, we propose a novel approach of applying machine learning, a branch of the field of artificial intelligence, to a variety of information concerning people’s lives (i.e., a lifelog). We asked a participant (a healthy young man) to record 55 lifelog items (e.g., positive mood, negative events, sleep time etc.) in his daily life for about eight months using smartphone apps and a smartwatch. We then constructed a predictor to estimate the degree of happiness from the multimodal lifelog data using a support vector machine, which achieved 82.6% prediction accuracy. This suggests that our approach can predict the behaviors that increase individuals’ happiness in their daily lives, thereby contributing to improvement in their happiness. Future studies examining the usability and clinical applicability of this approach would benefit from a larger and more diverse sample size.

Assessment of health related issues in individuals’, couples’, and families’ daily life

2008 ◽  
Vol 16 (3) ◽  
pp. 146-149 ◽  
Author(s):  
Meinrad Perrez ◽  
Michael Reicherts ◽  
Yves Hänggi ◽  
Andrea B. Horn ◽  
Gisela Michel ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
Daily Life ◽  
Ecological Validity ◽  
Reliability And Validity ◽  
Well Being ◽  
Computer Assisted ◽  
Emotional States ◽  
Self Reports ◽  
Health Related ◽  
Lower Variability

Abstract. Most research in health psychology is based on retrospective self reports, which are distorted by recall biases and have low ecological validity. To overcome such limitations we developed computer assisted diary approaches to assess health related behaviours in individuals’, couples’ and families’ daily life. The event- and time-sampling-based instruments serve to assess appraisals of the current situation, feelings of physical discomfort, current emotional states, conflict and emotion regulation in daily life. They have proved sufficient reliability and validity in the context of individual, couple and family research with respect to issues like emotion regulation and health. As examples: Regarding symptom reporting curvilinear pattern of frequencies over the day could be identified by parents and adolescents; or psychological well-being is associated with lower variability in basic affect dimensions. In addition, we report on preventive studies to improve parental skills and enhance their empathic competences towards their baby, and towards their partner.

Challenges and opportunities for the affective sciences.

2018 ◽  
Author(s):  
Andrew S. Fox ◽  
Regina Lapate ◽  
Alexander J. Shackman ◽  
Richard J Davidson
Keyword(s):  
Well Being ◽  
Human Condition ◽  
Emotional States ◽  
Quarter Century ◽  
Core Feature ◽  
Challenges And Opportunities ◽  
The Mind ◽  
The Impact ◽  
Improved Methods ◽  
Health And Well Being

Emotion is a core feature of the human condition, with profound consequences for health, wealth, and wellbeing. Over the past quarter-century, improved methods for manipulating and measuring different features of emotion have yielded steady advances in our scientific understanding emotional states, traits, and disorders. Yet, it is clear that most of the work remains undone. Here, we highlight key challenges facing the field of affective sciences. Addressing these challenges will provide critical opportunities not just for understanding the mind, but also for increasing the impact of the affective sciences on public health and well-being.

The link between coronavirus, anxiety, and religious beliefs in the United States and United Kingdom

2020 ◽  
Author(s):  
Francesco Rigoli
Keyword(s):  
United States ◽  
United Kingdom ◽  
Religious Beliefs ◽  
Well Being ◽  
The United States ◽  
Christian Faith ◽  
Stressful Event ◽  
Cultural Implications ◽  
The Impact

Research has shown that stress impacts on people’s religious beliefs. However, several aspects of this effect remain poorly understood, for example regarding the role of prior religiosity and stress-induced anxiety. This paper explores these aspects in the context of the recent coronavirus emergency. The latter has impacted dramatically on many people’s well-being; hence it can be considered a highly stressful event. Through online questionnaires administered to UK and USA citizens professing either Christian faith or no religion, this paper examines the impact of the coronavirus crisis upon common people’s religious beliefs. We found that, following the coronavirus emergency, strong believers reported higher confidence in their religious beliefs while non-believers reported increased scepticism towards religion. Moreover, for strong believers, higher anxiety elicited by the coronavirus threat was associated with increased strengthening of religious beliefs. Conversely, for non-believers, higher anxiety elicited by the coronavirus thereat was associated with increased scepticism towards religious beliefs. These observations are consistent with the notion that stress-induced anxiety enhances support for the ideology already embraced before a stressful event occurs. This study sheds light on the psychological and cultural implications of the coronavirus crisis, which represents one of the most serious health emergencies in recent times.

Human Stress Recovery during Exposure to Geranium Visual Stimuli

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 505b-505 ◽  
Author(s):  
Eunhee Kim ◽  
Richard H. Mattson
Keyword(s):  
Recovery Period ◽  
Visual Stimuli ◽  
Stress Recovery ◽  
Emotional States ◽  
Finger Skin Temperature ◽  
Physiological Indicators ◽  
Treatment Groups ◽  
Human Situation ◽  
Human Stress ◽  
Finger Skin

Evaluating human psychophysiological responses to plant visual stimuli provides a clearer understanding of factors within plant environments that enhance or maximize recovery from stress. Advances in physiological monitoring technology allow continuous recording and more-refined data collection of human responses to environmental stimuli. The objective of this study was to compare effects on stress recovery by exposures to geranium visual stimuli following an induced stressor, by measuring changes in physiological indicators and emotional states. One-hundred-fifty college students were randomly assigned to one of three treatment groups: red-flowering geraniums, non-flowering geraniums, or no geraniums. Each student viewed a 10-min film of a stressful human situation following a 5-min baseline, then was exposed to an assigned treatment setting during a 5-min recovery period. Continuous physiological measurements were taken of brainwave activities (EEG), skin conductance (EDR), and finger skin temperature. Self-rating scores of subjects' feelings were taken using the Zuckerman Inventory of Personal Reactions. Comparisons among treatment groups will be discussed based on gender and other demographic factors.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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