A Multivariate Signal Analysis of a Sensing Platform Prototype for Stress Detection

Author(s):  
Andrés Arcentales V. ◽  
Roberto Daniel Triviño ◽  
Celena Betancourt M. ◽  
Iván Yépez ◽  
Darwin Alulema ◽  
...  
2021 ◽  
Vol 5 (2) ◽  
pp. 233-244
Author(s):  
I. Ladakis ◽  
I. Chouvarda

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


Nanoscale ◽  
2021 ◽  
Author(s):  
WONIL NAM ◽  
Wansun Kim ◽  
Wei Zhou ◽  
Eun-Ah You

We report a digital surface-enhanced Raman spectroscopy (SERS) sensing platform using the arrays of 3D nanolaminate plasmonic crystals (NLPC) coupled with Au nanoparticles and the digital (on/off) SERS signal analysis...


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


2012 ◽  
Vol 17 (4) ◽  
pp. 319-326 ◽  
Author(s):  
Zbigniew Chaniecki ◽  
Krzysztof Grudzień ◽  
Tomasz Jaworski ◽  
Grzegorz Rybak ◽  
Andrzej Romanowski ◽  
...  

Abstract The paper presents results of the scale-up silo flow investigation in based on accelerometer signal analysis and Wi-Fi transmission, performed in distributed laboratory environment. Prepared, by the authors, a set of 8 accelerometers allows to measure a three-dimensional acceleration vector. The accelerometers were located outside silo, on its perimeter. The accelerometers signal changes allowed to analyze dynamic behavior of solid (vibrations/pulsations) at silo wall during discharging process. These dynamic effects are caused by stick-slip friction between the wall and the granular material. Information about the material pulsations and vibrations is crucial for monitoring the interaction between silo construction and particle during flow. Additionally such spatial position of accelerometers sensor allowed to collect information about nonsymmetrical flow inside silo.


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