Physiological Signals Based Automobile Drivers’ Stress Levels Detection Using Shape and Texture Feature Descriptors: An Experimental Study

2018 ◽  
pp. 436-447
Author(s):  
Abdultaofeek Abayomi ◽  
Oludayo O. Olugbara ◽  
Delene Heukelman ◽  
Emmanuel Adetiba
Keyword(s):  
Experimental Study ◽  
Texture Feature ◽  
Physiological Signals ◽  
Feature Descriptors ◽  
Stress Levels

Experimental Study on Flexural Fatigue Property of Ultra-High Toughness Cementitious Composites

2010 ◽  
Vol 150-151 ◽  
pp. 1369-1378 ◽  
Author(s):  
Shi Lang Xu ◽  
Wen Liu
Keyword(s):  
Experimental Study ◽  
Fatigue Loading ◽  
Fiber Reinforced Concrete ◽  
Cementitious Composites ◽  
Multiple Cracks ◽  
Steel Fiber Reinforced Concrete ◽  
Flexural Fatigue ◽  
Fatigue Stress ◽  
High Toughness ◽  
Stress Levels

This paper presents an experimental study on the flexural fatigue characteristics of Ultra-High Toughness Cementitious Composites (UHTCC), in contrast with plain concrete and Steel Fiber Reinforced Concrete (SFRC) which have similar compressive strength. The results show that UHTCC improves fatigue life and exhibits a bi-linear fatigue stress-life relationship. The deflection ability, failure characteristics of UHTCC were investigated in the tests. It was observed that, similar to static loading situation, multiple cracks were formed under fatigue loading, while the number of cracks decreased with the degradation of stress levels. For this reason, the deformability is much weaker at lower fatigue stress levels than that at higher stress levels. Moreover, the failure section is divided into three different districts, and the proportion of fiber rupture to fiber pullout is different under different stress levels.

Relationships between physiological signals and stress levels in the case of automated technology failure

2019 ◽  
Vol 1 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Celeste Branstrom ◽  
Heejin Jeong ◽  
Jaehyun Park ◽  
Byung-Cheol Lee ◽  
Jangwoon Park
Keyword(s):  
Physiological Signals ◽  
Stress Levels ◽  
Automated Technology

scholarly journals Real-Time Monitoring of Passenger’s Psychological Stress

2019 ◽  
Vol 11 (5) ◽  
pp. 102
Author(s):  
Gaël Vila ◽  
Christelle Godin ◽  
Oumayma Sakri ◽  
Etienne Labyt ◽  
Audrey Vidal ◽  
...  
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
High Stress ◽  
Ambulatory Monitoring ◽  
Real Life ◽  
Smartphone Application ◽  
Physiological Signals ◽  
Sensor Data ◽  
Moderate Activity ◽  
Stress Levels

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

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