scholarly journals Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

2021 ◽  
Vol 12 ◽  
Author(s):  
Keisuke Izumi ◽  
Kazumichi Minato ◽  
Kiko Shiga ◽  
Tatsuki Sugio ◽  
Sayaka Hanashiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rating Scale ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Well Being ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Body Motion ◽  
Study Results ◽  
The Relationship

Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being.Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.Registration: UMIN000036814

scholarly journals Quantification of stress and well-being using pulse, speech, and electrodermal data: Study concept and design

2020 ◽  
Author(s):  
Keisuke Izumi ◽  
Kazumichi Minato ◽  
Kiko Shiga ◽  
Tatsuki Sugio ◽  
Sayaka Hanashiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Electrodermal Activity ◽  
Japanese Version ◽  
Well Being ◽  
Biological Data ◽  
Satisfaction With Life Scale ◽  
List Type ◽  
Study Results ◽  
The Relationship

ABSTRACTIntroductionMental disorders are a leading cause of disability worldwide and, among mental disorders, major depressive disorder was highly ranked in years lived with disability. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.Methods and analysisThis is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: a) participants’ background characteristics; b) participants’ biological data during the 4-week observation period using sensing devices such as a camera built into or connected to the computer (pulse wave data extracted from the facial video images), a microphone built into or connected to their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); c) stress, well-being, and depression rating scale assessment data (New Occupational Stress Questionnaire, Perceived Stress Scale, Satisfaction With Life Scale, Japanese version of Positive and Negative Affect Schedule, Japanese Flourishing Scale, Subjective Well-being / Ideal Happiness, and Japanese version of Patient Health Questionnaire-9). The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants’ vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.Ethics and disseminationCollected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.RegistrationUMIN000036814STRENGTHS AND LIMITATIONS OF THIS STUDYThis study evaluates stress and well-being using biomarkers such as heart rate, acoustic characteristics, and electrodermal activity.This study measures biomarkers over a long-term four-week period.This study will lead to the development of a machine learning algorithm to determine people’s optimal levels of stress and well-being.This is a government-funded study in which many different companies and institutions collaborated for a common purpose.There is a possibility that the algorithm’s prediction accuracy level may decrease when it is applied to demographic groups other than those studied here.

scholarly journals Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility

2020 ◽  
Vol 12 (11) ◽  
pp. 4748
Author(s):  
Minrui Zheng ◽  
Wenwu Tang ◽  
Akinwumi Ogundiran ◽  
Jianxin Yang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Simulation Modeling ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Simulation Models ◽  
Machine Learning Algorithms ◽  
Spatial Simulation ◽  
Study Region ◽  
The Relationship

Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.

scholarly journals Dysgraphia detection through machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter Drotár ◽  
Marek Dobeš
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Written Expression ◽  
Learning Approach ◽  
Adaptive Boosting ◽  
Adaboost Algorithm ◽  
Machine Learning Approach ◽  
Automated Procedures

AbstractDysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.

scholarly journals Pain assessment tool with Electrodermal Activity for post-operative patients: A method validation study (Preprint)

2020 ◽  
Author(s):  
Seyed Amir Hossein Aqajari ◽  
Rui Cao ◽  
Emad Kasaeyan Naeini ◽  
Michael-David Calderon ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pain Assessment ◽  
Assessment Tool ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Adult Patients ◽  
Self Report ◽  
Physiological Data ◽  
Pain Models

BACKGROUND Accurate objective pain assessment is required in the healthcare domain and clinical settings for appropriate pain management. Automated objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, in particular for those patients who are unable to self-report. Galvanic Skin Response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify the features of emotional states and anxiety induced by varying pain levels. In this study, we used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, we are the first work building pain models using postoperative adult patients instead of healthy subjects. OBJECTIVE The goal of this paper is to present an automatic pain assessment tool using GSR signals to predict different pain intensities in non-communicative postoperative patients. METHODS The study was designed to collect biomedical data from post-operative patients reporting moderate to high pain levels. 25 subjects were recruited with the age range of 23 to 89. First, a Transcutaneous Electrical Nerve Stimulation (TENS) unit was employed to obtain patients' baselines. In the second part, the Empatica E4 wristband was attached to patients while they were performing low intensity activities. Patient self-report based on the NRS was used to record pain intensities used to correlate with the objective measured data. The labels were downsampled from 11 pain levels to 5 different pain intensities including the baseline. Two different machine learning algorithms were used to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. RESULTS Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline (BL) vs. Pain Level (PL) 1, BL vs. PL2, BL vs. PL3, and BL vs. PL4). Our models achieved the higher accuracy for the first three pain models in comparison with BioVid paper approach despite the challenges in analyzing real patient data. For BL vs. PL1, BL vs. PL2, and BL vs. PL4, the highest prediction accuracies were achieved when using a Random Forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs. PL3, we achieved the accuracy of 72.1 using a K-nearest neighbors classifier. CONCLUSIONS We are the first to propose and validate the pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. INTERNATIONAL REGISTERED REPORT RR2-10.2196/17783

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Acculturative stress and life satisfaction of Chinese rural-to-urban migrants

2020 ◽  
Vol 48 (11) ◽  
pp. 1-11
Author(s):  
Dekuo Liang ◽  
Lei Wang ◽  
Liying Xia ◽  
Dawei Xu
Keyword(s):  
Social Support ◽  
Life Satisfaction ◽  
Acculturative Stress ◽  
Perceived Social Support ◽  
Well Being ◽  
Self Esteem ◽  
Construction Sites ◽  
Urban Migrants ◽  
Study Results ◽  
The Relationship

Little is known regarding the life satisfaction of rural-to-urban migrants in China. In this study we assessed whether self-esteem and perceived social support mediated the association between rural-to-urban migrants' acculturative stress and life satisfaction. We use convenience sampling to recruit 712 migrants who were employed at construction sites in Nanjing for the study. Results reveal that acculturative stress was negatively related to self-esteem, perceived social support, and life satisfaction; self-esteem was positively associated with perceived social support and life satisfaction; and perceived social support was a significant and positive predictor of life satisfaction. In addition, we found that self-esteem and perceived social support partially mediated the relationship between acculturative stress and life satisfaction. Our findings provide a better understanding of life satisfaction over the course of migration, and add to knowledge of psychological well-being and mental health among rural-to-urban migrants in China.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Insights into therapeutic targets and biomarkers using integrated multi-‘omics’ approaches for dilated and ischemic cardiomyopathies

2021 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Neringa Burokienė
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Development Stage ◽  
Biological Data ◽  
Specific Gene ◽  
Tissue Remodelling ◽  
Pharmacological Management

Abstract At present, heart failure (HF) treatment only targets the symptoms based on the left ventricle dysfunction severity; however, the lack of systemic ‘omics’ studies and available biological data to uncover the heterogeneous underlying mechanisms signifies the need to shift the analytical paradigm towards network-centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover HF-specific networks and potential therapeutic targets or biomarkers. We also aimed to address the issue of dealing with a limited number of samples and to show how appropriate statistical models, enrichment with other datasets as well as machine learning-guided analysis can aid in such cases. Furthermore, we elucidated specific gene expression profiles using transcriptomic and mined data from public databases. This was achieved using the two-step machine learning algorithm to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system, which has also been introduced in this study. The described methodology could be very useful for the target or biomarker selection and evaluation during the pre-clinical therapeutics development stage as well as disease progression monitoring. In addition, the present study sheds new light into the complex aetiology of HF, differentiating between subtle changes in dilated cardiomyopathies (DCs) and ischemic cardiomyopathies (ICs) on the single cell, proteome and whole transcriptome level, demonstrating that HF might be dependent on the involvement of not only the cardiomyocytes but also on other cell populations. Identified tissue remodelling and inflammatory processes can be beneficial when selecting targeted pharmacological management for DCs or ICs, respectively.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

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