Mental Workload Assessment During Physical Activity Using Non-linear Movement Artefact Robust Electroencephalography Features

2019 ◽  
Author(s):  
Abhishek Tiwari ◽  
Isabela Albuquerque ◽  
Jean-Francois Gagnon ◽  
Daniel Lafond ◽  
Mark Parent ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mental Workload ◽  
Movement Artefact ◽  
Non Linear ◽  
Linear Movement

scholarly journals Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 783 ◽  
Author(s):  
Abhishek Tiwari ◽  
Isabela Albuquerque ◽  
Mark Parent ◽  
Jean-François Gagnon ◽  
Daniel Lafond ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mental Workload ◽  
Real Life ◽  
Heart Beat ◽  
Activity Levels ◽  
Safety Hazards ◽  
Constant Attention ◽  
Multi Scale ◽  
Entropy Measures ◽  
Ecg Artifacts

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24 . 41 % in accuracy and of 27 . 97 % in F1 score can be achieved even at high activity levels.

Physical activity volume in relation to risk of atrial fibrillation. A non-linear meta-regression analysis

2018 ◽  
Vol 25 (8) ◽  
pp. 857-866 ◽  
Author(s):  
Cristian Ricci ◽  
Federico Gervasi ◽  
Maddalena Gaeta ◽  
Cornelius M Smuts ◽  
Aletta E Schutte ◽  
...  
Keyword(s):  
Physical Activity ◽  
Atrial Fibrillation ◽  
Relative Risk ◽  
Vigorous Physical Activity ◽  
Response Analysis ◽  
Metabolic Equivalents ◽  
Increased Risk ◽  
Non Linear ◽  
Meta Regression ◽  
Physical Activity Volume

Background Light physical activity is known to reduce atrial fibrillation risk, whereas moderate to vigorous physical activity may result in an increased risk. However, the question of what volume of physical activity can be considered beneficial remains poorly understood. The scope of the present work was to examine the relation between physical activity volume and atrial fibrillation risk. Design A comprehensive systematic review was performed following the PRISMA guidelines. Methods A non-linear meta-regression considering the amount of energy spent in physical activity was carried out. The first derivative of the non-linear relation between physical activity and atrial fibrillation risk was evaluated to determine the volume of physical activity that carried the minimum atrial fibrillation risk. Results The dose–response analysis of the relation between physical activity and atrial fibrillation risk showed that physical activity at volumes of 5–20 metabolic equivalents per week (MET-h/week) was associated with significant reduction in atrial fibrillation risk (relative risk for 19 MET-h/week = 0.92 (0.87, 0.98). By comparison, physical activity volumes exceeding 20 MET-h/week were unrelated to atrial fibrillation risk (relative risk for 21 MET-h/week = 0.95 (0.88, 1.02). Conclusion These data show a J-shaped relation between physical activity volume and atrial fibrillation risk. Physical activity at volumes of up to 20 MET-h/week is associated with reduced atrial fibrillation risk, whereas volumes exceeding 20 MET-h/week show no relation with risk.

scholarly journals Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm

2011 ◽  
Vol 105 (11) ◽  
pp. 1681-1691 ◽  
Author(s):  
Kazunori Ohkawara ◽  
Yoshitake Oshima ◽  
Yuki Hikihara ◽  
Kazuko Ishikawa-Takata ◽  
Izumi Tabata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Regression ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Daily Physical Activity ◽  
Triaxial Accelerometer ◽  
Validation Group ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Non Linear

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

scholarly journals Effects of Heat Stress on Heart Rate Variability in Free-Moving Sheep and Goats Assessed With Correction for Physical Activity

2021 ◽  
Vol 8 ◽  
Author(s):  
Kaho Kitajima ◽  
Kazato Oishi ◽  
Masafumi Miwa ◽  
Hiroki Anzai ◽  
Akira Setoguchi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Heat Stress ◽  
Small Ruminants ◽  
Cardiac Activity ◽  
Heart Beat ◽  
Invasive Technique ◽  
Sheep And Goats ◽  
Non Linear

Heart rate variability (HRV) is the heart beat-to-beat variation under control of the cardiovascular function of animals. Under stressed conditions, cardiac activity is generally regulated with an upregulated sympathetic tone and withdrawal of vagal tone; thus, HRV monitoring can be a non-invasive technique to assess stress level in animals especially related to animal welfare. Among several stress-induced factors, heat stress is one of the most serious causes of physiological damage to animals. The aim of this study was to assess the effects of heat stress on HRV in small ruminants under free-moving conditions. In three experimental periods (June, August, and October), inter-beat intervals in sheep and goats (three for each) in two consecutive days were measured. HRV parameters were calculated from the inter-beat interval data by three types of analyses: time domain, frequency domain, and non-linear analyses. The temperature–humidity index (THI) was used as an indicator of heat stress, and vectorial dynamic body acceleration (VeDBA) was calculated to quantify the physical activity of the animals tested. First, we investigated correlations of THI and VeDBA with HRV parameters; subsequently, THI was divided into five categories according to the values obtained (≤ 65, 65–70, 70–75, 75–80, and &gt;80), and the effects of the THI categories on HRV parameters were investigated with and without correcting for the effects of physical activity based on the VeDBA. The results indicated that HRV significantly decreased with increasing THI and VeDBA. For non-linear HRV parameters that were corrected for the effects of physical activity, it was suggested that there would be a threshold of THI around 80 that strongly affected HRV; high heat stress can affect the autonomic balance of animals non-linearly by inducing the sympathetic nervous system. In conclusion, to assess psychophysiological conditions of unrestrained animals by HRV analysis, the confounding effect of physical activity on HRV should be minimized for a more precise interpretation of the results.

scholarly journals Mental Workload Alters Heart Rate Variability, Lowering Non-linear Dynamics

2019 ◽  
Vol 10 ◽  
Author(s):  
Stéphane Delliaux ◽  
Alexis Delaforge ◽  
Jean-Claude Deharo ◽  
Guillaume Chaumet
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Workload ◽  
Linear Dynamics ◽  
Non Linear

Physical activity and stroke among women – A non-linear relationship

2021 ◽  
pp. 106485
Author(s):  
C.J. MacDonald ◽  
A.L. Madika ◽  
R. Gomes ◽  
G. Severi ◽  
Igor Sibon ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Relationship ◽  
Non Linear

scholarly journals Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258748
Author(s):  
Dmitrii Borisevich ◽  
Theresia M. Schnurr ◽  
Line Engelbrechtsen ◽  
Alexander Rakitko ◽  
Lars Ängquist ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Environment Interaction ◽  
Linear Interaction ◽  
Polygenic Risk ◽  
Non Linear

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

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