Effects of fatigue on the physiological parameters of labor employees

Meng, Jianfeng; Bi, Zhao; Ma, Yunfeng; Ji, Yiyu; Nie, Bai

doi:10.1007/s11069-014-1235-z

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…They then used a neural network (NN) algorithm, obtaining an accuracy of 90%. Compared to the experimental results between wakefulness and drowsiness, Meng et al [25] however found that subjects' blood pressure and respiratory rate, instead of heart rate, had changed significantly. Boonleng et al [27] developed a mobile system consisting of five wearable sensors that were placed in the best positions to detect vigilance.…”

Section: ) Electrocardiogram (Ecg) and Electromyogram (Emg)mentioning

confidence: 88%

“…In recent years, many methods have used Electrocardiogram (ECG) [11], [23]- [25] and Electromyogram (EMG) [26], [27] signals to quantitatively analyze levels of vigilance. For example, Patel et al [24] proposed a strategy that utilized the bandpass filter (BPF) for pre-processing raw ECG signal and extracted features by fast Fourier transform (FFT).…”

Section: ) Electrocardiogram (Ecg) and Electromyogram (Emg)mentioning

confidence: 99%

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A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG

Sun

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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In recent years, it has been observed that there is an increasing rate of road accidents due to the low vigilance of drivers. Thus, the estimation of drivers' vigilance state plays a significant role in Public Transportation Safety (PTS). We have adopted a feature fusion strategy that combines the electroencephalogram (EEG) signals collected from various sites of the human brain, including forehead, temporal, and posterior and forehead electrooculogram (forehead-EOG) signals, to address this factor. The level of vigilance is predicted through a new learning model known as double-layered neural network with subnetwork nodes (DNNSN), which comprises several subnetwork nodes, and each node in turn is composed of many hidden nodes that have various capabilities of feature selection (dimension reduced), feature learning, etc. The proposed single modality that uses only forehead-EOG signal exhibits a mean root-mean-square error (RMSE) of 0.12 and a mean Pearson product-moment correlation coefficient (COR) of 0.78. On one hand, an EEG signal achieved a mean RMSE of 0.13 and a mean COR of 0.72. Whereas, on the other, the proposed multimodality achieved values of 0.09 and 0.85 for the mean RMSE and the mean COR, respectively. Experimental results show that the proposed DNNSN with multimodality fusion outperforms the model with single modality for vigilance estimation due to the complementary information between forehead-EOG and EEG. After a favorable learning rate was applied to the input layer, the mean RMSE/COR improved to 0.11/0.79, 0.12/0.74, and 0.08/0.86, respectively. Hence, this quantitative analysis proves that the proposed method provides better feasibility and efficiency learning capability and surmounts other state-of-theart techniques.

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Section: ) Electrocardiogram (Ecg) and Electromyogram (Emg)mentioning

confidence: 88%

Section: ) Electrocardiogram (Ecg) and Electromyogram (Emg)mentioning

confidence: 99%

A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG

Sun

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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“…Many disorders and/or stimulus administrations generate alterations in respiratory activity. Respiration Rate (RR) is a vital sign sensitive to different pathological conditions and/or stressors including emotional stress, cognitive load, heat, cold, physical effort and exercise-induced fatigue [ 27 ]. Moreover, it can be used to assess the psychophysiological state of a user even if its modifications are much slower than those exhibited by other physiological parameters.…”

Section: Methodsmentioning

confidence: 99%

A Classification Method for Workers’ Physical Risk

Tamantini

Rondoni

Cordella

et al. 2023

Sensors

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In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers’ risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker’s complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

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“…The ECG carries information about a person's vigilance state. Hence, HRV measures could potentially be used to predict when an individual is at increased risk of attentional failure [16]. Respiration rate and blood pressure have a significant difference before and after fatigue: respiration rate decreased after fatigue.…”

Section: )mentioning

confidence: 99%

Research of Human Fatigue and Measurement Parameters for Workability Assessment

Erins

Minejeva

Markovičs

et al. 2019

ETR

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Human fatigue is reduced working capability for certain period of time as the result of unusual or prolonged workload. Fatigue arises when the body’s energy requirements exceed its supply. Fatigue first manifests as reduced concentration capability causing movement coordination and precision disruption leading to decreasing workability. Fatigue is an object of research in physiology, psychology, work ergonomics, medicine, and biotechnology where each domain has a focus on mental fatigue. The functional state in the context of professional activity is defined as a complex of characteristics of functions and qualities that determine the fulfilment of labour activity. Furthermore, a comprehensive estimation of subject functional state in combination with other factors like subject self-assessment and objective performance tests (cognitive load tests) is a necessary input for the evaluation of workability and efficiency on task. The heterogeneous nature of fatigue as a systemic manifestation requires analysis of multiple key parameters which are relevant to the specific type. The current feasibility study focuses on human biological signal from electrical activity of heart, brain, muscles and skin potentials as well as temperature, position, and respiration to obtain diagnostic parameters reflecting the state of cardiovascular, muscles, and central nervous systems for physiological monitoring of vital signs. The fatigue physiological parameter and feature formalization aim to support the development of a platform with complex passive multi-level fatigue monitoring system and workability evaluation system designed in order to provide an integrated service.

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Effects of fatigue on the physiological parameters of labor employees

Cited by 13 publications

References 19 publications

A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG

A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG

A Classification Method for Workers’ Physical Risk

Research of Human Fatigue and Measurement Parameters for Workability Assessment

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