Documenting the Temporal Pattern of Fatigue Development

Relationship between physical fatigue and mental fatigue based on multimodal measurement under different load levels

Based on multimodal measurement methods of NASA task load index (NASA-TLX), task performance, surface electromyography (sEMG), heart rate (HR), and functional near-infrared spectroscopy (fNIRS), this study conducted experimental measurements and analyses under 16 different load levels of physical fatigue and mental fatigue combination conditions. This study observed the interaction between physical fatigue and mental fatigue at different levels, and at the subjective level, the effect of physical fatigue on mental fatigue was greater than that of mental fatigue on physical fatigue. Secondly, the results of fNIRS analysis showed that the premotor cortex is affected by physical fatigue, and the dorsolateral prefrontal cortex is affected by mental fatigue. Finally, this study constructed a fatigue classification model with an accuracy of 95.3%, which takes multimodal physiological data as input and 16 fatigue states as output. The research results will provide a basis for fatigue analysis, evaluation, and improvement in complex working situations.

Muscle fatigue tracking during dynamic elbow flexion-extension movements with a varying hand load

Muscle fatigue monitoring, an important element in a fatigue risk management process, can help optimize work intensity and reduce risks for musculoskeletal injuries. An experiment was conducted to determine whether myoelectric manifestations of muscle fatigue can reflect the pace of fatigue development associated with varying load intensity. Twenty male participants performed elbow flexion-extension movements with alternating hand loads (2 kg vs. 1 kg) for 16 min. The pace of fatigue in the biceps brachii in response to load variation was quantified by electromyographic (EMG) fatigue measures collected during the dynamic elbow flexion-extension movements and periodic submaximal isometric elbow flexion trials. The isometric and dynamic EMG measures, except for the amplitude of dynamic EMG, indicated fatigue development during the 2-kg isotonic movements and partial recovery with the 1 kg load. Study results suggest the potential of EMG measures for fatigue monitoring during dynamic work tasks with varying load intensity.

Is there a u-shaped relationship between load levels and fatigue and recovery? An examination of possible mechanisms

In a previous study, an unexpected u-shaped relationship was observed between load level and fatigue/recovery responses. Moderate load levels resulted in lower perceived discomfort, pain, and fatigue, and shorter recovery times compared to either low or high load levels. This phenomenon has been reported in other studies, but no article has examined the possible mechanisms that might explain this u-shaped relationship. In this paper, we re-examined the previously published data and found that the phenomenon does not appear to be due to the experimental artefact; the u-shape may be due to unexpectedly lower fatigue effects at moderate loads, and higher fatigue effects at lower loads. We then conducted a literature review and identified several possible physiological, perceptual, and biomechanical explanatory mechanisms. No single mechanism explains the entirety of the phenomenon. Further research is needed on the relationship between work exposures, fatigue, and recovery, and the mechanisms related to the u-shaped relationship.Practitioner summary: We examine a previously observed u-shaped relationship between load level and fatigue/recovery, where moderate force resulted in lower perceived fatigue and shorter recovery times. A u-shaped fatigue response suggests that simply minimising load levels might not be an optimal approach to reduce the risk of workplace injuries.

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms "fatigue," "drowsiness," "vigilance," or "alertness" in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (T_sk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

Effects of Fatigue and Grit on Club Sports Coaches

The objective of this research is to identify the level of general fatigue (FG), physical fatigue (FF) and concentration/motivation (C/M) in sports coaches. Two components of grit, consistency of interest (CI) and perseverance in effort (PE), are also assessed. The possible effects of sex, age, marital status, employment contract, work dedication and grit on FG, FF and C/M in sports coaches are examined. This cross-sectional study analyses 335 sports club coaches (21.2% women, 78.8% male) with a mean age of 29.88 (SD = 9.97) years, at a significance level of p < 0.05 for all analyses. Different aspects of fatigue were determined using the Spanish translation of the Multidimensional Fatigue Inventory-20 (IMF-20). The Grit-S scale was used to measure the ability to persevere, have passion and commit. The results indicated that men scored higher in FF, C/M and PE, while women obtained higher values in FG and CI. Non-contract coaches had higher FG, CI and PE, while coaches with contracts scored higher on C/M and FF. In conclusion, coaches with higher CI had higher FG, and high levels of PE were associated with low FG levels.

Examining the fatigue-quality relationship in manufacturing

A recent systematic review identified 73 empirical studies that linked human factors (HF) with manufacturing quality. Human fatigue was noted as a frequent (n = 26) issue in the HF-quality relationship - a finding that warrants closer examination. We extend this review by investigating the relationship between fatigue and manufacturing quality by identifying how fatigue has been conceptualized and measured, and we attempted to quantify their relationship. From the original database, 12 of 26 relevant studies (46%) indicated that physical fatigue was the primary contributor to observed quality deficits. There was a positive relationship between fatigue and quality deficits, with fatigue accounting up to 42% of the variance. More studies are needed to improve the resolution, specificity, and power of these analyses. This study sheds light on the role of HF and human fatigue effects on manufacturing quality with macroergonomic implications for embedding HF aspects into design and quality assurance processes.