scholarly journals To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection.

2017 ◽  
pp. 643-646 ◽  
Author(s):  
Rossana Castaldo ◽  
Luis Montesinos ◽  
Paolo Melillo ◽  
Sebastiano Massaro ◽  
Leandro Pecchia
Keyword(s):  
Mental Stress ◽  
Stress Detection ◽  
Wearable Sensing ◽  
Hrv Analysis

scholarly journals Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
R. Castaldo ◽  
L. Montesinos ◽  
P. Melillo ◽  
C. James ◽  
L. Pecchia
Keyword(s):  
Mental Stress ◽  
Real Life ◽  
Stress Detection ◽  
Short Term

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 Unsupervised Learning for Mental Stress Detection

2018 ◽  
Author(s):  
Dorien Huysmans ◽  
Elena Smets ◽  
Walter De Raedt ◽  
Chris Van Hoof ◽  
Katleen Bogaerts ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Mental Stress ◽  
Stress Detection

Personalized mental stress detection with self-organizing map: From laboratory to the field

2020 ◽  
Vol 124 ◽  
pp. 103935
Author(s):  
Jaakko Tervonen ◽  
Sampsa Puttonen ◽  
Mikko J. Sillanpää ◽  
Leila Hopsu ◽  
Zsolt Homorodi ◽  
...  
Keyword(s):  
Mental Stress ◽  
Self Organizing Map ◽  
Stress Detection ◽  
Self Organizing
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