scholarly journals Overview of Biosignal Analysis Methods for the Assessment of Stress

2021 ◽  
Vol 5 (2) ◽  
pp. 233-244
Author(s):  
I. Ladakis ◽  
I. Chouvarda
Keyword(s):  
Signal Analysis ◽  
Negative Impact ◽  
Research Community ◽  
Physiological Signals ◽  
Human Organism ◽  
Stress Detection ◽  
Biosignal Analysis ◽  
Analysis Methods ◽  
Psychological Balance ◽  
Assessment Of Stress

Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation   of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems.  Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PDF

scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

scholarly journals Self-Activation as a Personal Resource in University Inclusive Educational Environment

2018 ◽  
Vol 7 (1) ◽  
pp. 62-77
Author(s):  
M.A. Odintsova ◽  
N. Radchikova
Keyword(s):  
Students With Disabilities ◽  
Inclusive Education ◽  
Negative Impact ◽  
Self Regulation ◽  
Subjective Assessment ◽  
Self Control ◽  
Personal Resources ◽  
Voluntary Activity ◽  
Personal Resource ◽  
Psychological Balance

In the sphere of higher education, which is considered one of the most promising in promoting inclusive ideas, great attention is paid to students’ personal resources, characterizing the person's internal voluntary activity: self-control, self-esteem, self-efficacy, self-management, self-knowledge, self-support, self-regulation, and self-activation. They are these personal resources that mediate the students' subjective assessment of the external situation (the absence of barriers, the availability of different types of support), weaken the negative impact of disability, preserve the psychological balance and motivate them to overcome difficulties. A study conducted on the Russian sample showed that inclusive education is effective for students with disabilities and is a motivating factor for their healthy peers. The data obtained is consistent with the results of studies in other countries and cultures

Observing sugar-beet quality using process and signal-analysis methods

1999 ◽  
Vol 7 (7) ◽  
pp. 881-890 ◽  
Author(s):  
A. Arenz ◽  
M. Reimann ◽  
E. Schnieder ◽  
U. Harten
Keyword(s):  
Sugar Beet ◽  
Signal Analysis ◽  
Analysis Methods

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.

EMoCy: Towards Physiological Signals-Based Stress Detection

2021 ◽  
Author(s):  
Armando Bellante ◽  
Letizia Bergamasco ◽  
Ana Bogdanovic ◽  
Noemi Gozzi ◽  
Lorenzo Gecchelin ◽  
...  
Keyword(s):  
Physiological Signals ◽  
Stress Detection
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