scholarly journals Portable System for Real-Time Detection of Stress Level

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2504 ◽  
Author(s):  
Jesus Minguillon ◽  
Eduardo Perez ◽  
Miguel Lopez-Gordo ◽  
Francisco Pelayo ◽  
Maria Sanchez-Carrion
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Mental Stress ◽  
Daily Life ◽  
Stress Detection ◽  
Skin Response ◽  
Portable System ◽  
Real Time Detection ◽  
Detection Of Stress

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

Real Time Mental Stress Detection Through Breath Analysis

2020 ◽  
pp. 403-410
Author(s):  
Paulo Santos ◽  
Peter Roth ◽  
Jorge M. Fernandes ◽  
Viktor Fetter ◽  
Valentina Vassilenko
Keyword(s):  
Real Time ◽  
Mental Stress ◽  
Breath Analysis ◽  
Stress Detection

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

scholarly journals ANALYSIS OF PROSPECTIVE PHYSICS TEACHERS� FEEDBACK ON ONLINE PEER-ASSESSMENT IMPLEMENTATION

2017 ◽  
Vol 13 (1) ◽  
pp. 41-48
Author(s):  
K. Khusaini
Keyword(s):  
Social Media ◽  
Real Time ◽  
Daily Life ◽  
Peer Assessment ◽  
Smart Phone ◽  
Physics Teachers ◽  
Back Ground ◽  
Online Peer Assessment ◽  
Teaching Physics

This study aimed to analyse prospective physics teachers feedback during the implementation of online peer-assessment in Teaching Physics in English course. Twenty prospective physics teachers participated in this study. They were familiar with the use of smart phone in their daily life. They tend to communicate using social media such as WhatsApp application. The students have practiced in using paper based peer-assessment in other courses, but they have not applied it in online method providing real time feedback and score. The implementation of online peer-assessment challenged the students to assess their peer objectively. The lecturers feedback influence students skills how to evaluate their peer performance. Several factors may influence the quality of students online peer-assessment such as students culture back ground, implementation of the online peer-assessment, and practicants performance.

Real-time mental stress detection based on smartwatch

2017 ◽  
Author(s):  
Lucio Ciabattoni ◽  
Francesco Ferracuti ◽  
Sauro Longhi ◽  
Lucia Pepa ◽  
Luca Romeo ◽  
...  
Keyword(s):  
Real Time ◽  
Mental Stress ◽  
Stress Detection

scholarly journals Intelligent Mobile Applications: A Systematic Mapping Study

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Taoufik Rachad ◽  
Ali Idri
Keyword(s):  
Mobile Technology ◽  
Mobile Applications ◽  
Daily Life ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
E Learning ◽  
Application Fields ◽  
Research Type

Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques.

scholarly journals Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4400
Author(s):  
Syed Faraz Naqvi ◽  
Syed Saad Azhar Ali ◽  
Norashikin Yahya ◽  
Mohd Azhar Yasin ◽  
Yasir Hafeez ◽  
...  
Keyword(s):  
Real Time ◽  
Mental Stress ◽  
State Of The Art ◽  
Reasonable Accuracy ◽  
Stress Assessment ◽  
Time Stress ◽  
Assessment Approach ◽  
Offline Processing ◽  
Aided Diagnosis

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

Real-Time Detection of Stress in 3D Tissue-Engineered Constructs Using NF-κB Activation in Transiently Transfected Human Dermal Fibroblast Cells

2007 ◽  
Vol 13 (5) ◽  
pp. 1013-1024 ◽  
Author(s):  
Irene Cantón ◽  
Umran Sarwar ◽  
E. Helen Kemp ◽  
Anthony J. Ryan ◽  
Sheila MacNeil ◽  
...  
Keyword(s):  
Real Time ◽  
Dermal Fibroblast ◽  
Human Dermal Fibroblast ◽  
Fibroblast Cells ◽  
Real Time Detection ◽  
Detection Of Stress
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