Task Load and Stress

Epps, Julien

doi:10.1002/9781118976005.ch11

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…They found that carrying a distributed load induced significantly higher HR responses than a compact load. In addition to HR, EDA is an important measure to assess exertion during physical and mental workload (Epps, 2018; Collet et al , 2014; Anwer et al , 2020). While many ergonomic studies have used EDA to objectively quantify mental fatigue (Gevins and Smith, 2003; Reimer and Mehler, 2011; Just et al , 2003), no study has used EDA to measure physical exertion during different load-carrying tasks, which may provide new insight into physiological changes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, these studies only assessed energy expenditure or oxygen consumption that may lead to exertion. Recently, heart rate (HR) and electrodermal activity (EDA) have been found as important indicators of physical and mental exertion (Epps, 2018; Collet et al , 2014; Anwer et al , 2020). Furthermore, carrying a load in one hand corresponds to a higher HR than carrying a load in two hands (Irion et al , 2010).…”

Section: Introductionmentioning

confidence: 99%

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Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Anwer

Antwi‐Afari

et al. 2021

ECAM

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PurposeSince construction workers often need to carry various types of loads in their daily routine, they are at risk of sustaining musculoskeletal injuries. Additionally, carrying a load during walking may disturb their walking balance and lead to fall injuries among construction workers. Different load carrying techniques may also cause different extents of physical exertion. Therefore, the purpose of this paper is to examine the effects of different load-carrying techniques on gait parameters, dynamic balance, and physiological parameters in asymptomatic individuals on both stable and unstable surfaces.Design/methodology/approachFifteen asymptomatic male participants (mean age: 31.5 ± 2.6 years) walked along an 8-m walkway on flat and foam surfaces with and without a load thrice using three different techniques (e.g. load carriage on the head, on the dominant shoulder, and in both hands). Temporal gait parameters (e.g. gait speed, cadence, and double support time), gait symmetry (e.g. step time, stance time, and swing time symmetry), and dynamic balance parameters [e.g. anteroposterior and mediolateral center of pressure (CoP) displacement, and CoP velocity] were evaluated. Additionally, the heart rate (HR) and electrodermal activity (EDA) was assessed to estimate physiological parameters.FindingsThe gait speed was significantly higher when the load was carried in both hands compared to other techniques (Hand load, 1.02 ms vs Head load, 0.82 ms vs Shoulder load, 0.78 ms). Stride frequency was significantly decreased during load carrying on the head than the load in both hands (46.5 vs 51.7 strides/m). Step, stance, and swing time symmetry were significantly poorer during load carrying on the shoulder than the load in both hands (Step time symmetry ration, 1.10 vs 1.04; Stance time symmetry ratio, 1.11 vs 1.05; Swing time symmetry ratio, 1.11 vs 1.04). The anteroposterior (Shoulder load, 17.47 mm vs Head load, 21.10 mm vs Hand load, −5.10 mm) and mediolateral CoP displacements (Shoulder load, −0.57 mm vs Head load, −1.53 mm vs Hand load, −3.37 ms) significantly increased during load carrying on the shoulder or head compared to a load in both hands. The HR (Head load, 85.2 beats/m vs Shoulder load, 77.5 beats/m vs No load, 69.5 beats/m) and EDA (Hand load, 14.0 µS vs Head load, 14.3 µS vs Shoulder load, 14.1 µS vs No load, 9.0 µS) were significantly larger during load carrying than no load.Research limitations/implicationsThe findings suggest that carrying loads in both hands yields better gait symmetry and dynamic balance than carrying loads on the dominant shoulder or head. Construction managers/instructors should recommend construction workers to carry loads in both hands to improve their gait symmetry and dynamic balance and to lower their risk of falls.Practical implicationsThe potential changes in gait and balance parameters during various load carrying methods will aid the assessment of fall risk in construction workers during loaded walking. Wearable insole sensors that monitor gait and balance in real-time would enable safety managers to identify workers who are at risk of falling during load carriage due to various reasons (e.g. physical exertion, improper carrying techniques, fatigue). Such technology can also empower them to take the necessary steps to prevent falls.Originality/valueThis is the first study to use wearable insole sensors and a photoplethysmography device to assess the impacts of various load carrying approaches on gait parameters, dynamic balance, and physiological measures (i.e. HR and EDA) while walking on stable and unstable terrains.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Anwer

Antwi‐Afari

et al. 2021

ECAM

View full text Add to dashboard Cite

show abstract

“…This segmentation correlates with understimulation leading to disengagement, optimal arousal corresponding to peak performance, and overarousal resulting in stress or errors. Both disengagement and excessive stress can negatively impact accuracy, productivity, and multitasking capabilities, highlighting the need for a delicate balance to maintain team efficacy [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between task load and performance, with the task load scale derived from the NASA-TLX as a percentage. Performance varies with task load, where lower performance is associated with disengagement (task load < 33%), optimal performance corresponds to peak engagement (task load 33-66%), and higher task load leads to stress and reduced performance (task load > 66%) [1,8,10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis

Korivand,

Galvani,

Ajoudani

et al. 2024

Sensors

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The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot’s speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration.

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A narrative review of immersive virtual reality’s ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload

et al. 2022

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This narrative review synthesizes and introduces 386 previous works about virtual reality-induced symptoms and effects by focusing on cybersickness, visual fatigue, muscle fatigue, acute stress, and mental overload. Usually, these VRISE are treated independently in the literature, although virtual reality is increasingly considered an option to replace PCs at the workplace, which encourages us to consider them all at once. We emphasize the context of office-like tasks in VR, gathering 57 articles meeting our inclusion/exclusion criteria. Cybersickness symptoms, influenced by fifty factors, could prevent workers from using VR. It is studied but requires more research to reach a theoretical consensus. VR can lead to more visual fatigue than other screen uses, influenced by fifteen factors, mainly due to vergence-accommodation conflicts. This side effect requires more testing and clarification on how it differs from cybersickness. VR can provoke muscle fatigue and musculoskeletal discomfort, influenced by fifteen factors, depending on tasks and interactions. VR could lead to acute stress due to technostress, task difficulty, time pressure, and public speaking. VR also potentially leads to mental overload, mainly due to task load, time pressure, and intrinsically due interaction and interface of the virtual environment. We propose a research agenda to tackle VR ergonomics and risks issues at the workplace.

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Task Load and Stress

Cited by 9 publications

References 46 publications

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis

A narrative review of immersive virtual reality’s ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload

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