Machine Learning Based Biosignals Mental Stress Detection

2021 ◽  
pp. 28-41
Author(s):  
Adel Ali Al-Jumaily ◽  
Nafisa Matin ◽  
Azadeh Noori Hoshyar
Keyword(s):  
Machine Learning ◽  
Mental Stress ◽  
Stress Detection

Mental stress detection using machine learning algorithm

2021 ◽  
Author(s):  
Aishwarya Bannore ◽  
Tejashree Gore ◽  
Apoorva Raut ◽  
Kiran Talele
Keyword(s):  
Machine Learning ◽  
Mental Stress ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Stress Detection

scholarly journals Analysis on Machine Learning Techniques for Stress Detection among Employees

2021 ◽  
Vol 10 (1) ◽  
pp. 35-37
Author(s):  
B. K. Kiranashree ◽  
V. Ambika ◽  
A. D. Radhika
Keyword(s):  
Machine Learning ◽  
Mental Stress ◽  
Detection Method ◽  
Physiological Parameters ◽  
Machine Learning Techniques ◽  
Health Issues ◽  
Stress Detection ◽  
Learning Techniques ◽  
Skin Response ◽  
Management Techniques

Mental stress is a common and major issue nowadays especially among working professional, because employees have family commitments with their over workload, target, achievements, etc. Stress tends various health issues like heart attack, stroke, depression, and suicide. Mental stress is not only in employees even normal people also face this problem but the employees has so many stress management techniques to manage the stress like yoga, meditation etc., but still employees suffer from the stress. Stress calculated by the Traditional stress detection method has two types of physiological parameters one is questionnaire format and another one is physiological signals based on Heart rate variability, galvanic skin response, BP, and electrocardiography, etc., Machine learning techniques are applied to analyze and anticipate stress in employees. In this paper, we mainly focus on different machine learning techniques and physiological parameters for stress detection.

scholarly journals Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1550
Author(s):  
Alexandros Liapis ◽  
Evanthia Faliagka ◽  
Christos P. Antonopoulos ◽  
Georgios Keramidas ◽  
Nikolaos Voros
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Binary Classification ◽  
Research Question ◽  
Classification Problem ◽  
Categorical Variables ◽  
Stress Detection ◽  
Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

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