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Empirical prediction model of psychophysiological responses of workers with respect to noise exposure based on random forest

Empirical prediction model of psychophysiological responses of workers with respect to noise... Evidence of the non-auditory effects of noise exposure on workers is growing. This study aimed to empirically present prediction models of psychophysiological responses of workers with respect to noise exposure. In this study, 169 male workers from typical industrial workrooms were asked to judge the mental workload, noise annoyance, and noise sensitivity during exposure to noise. Two main physiological responses that include heart rate and blood pressure were also measured. Noise exposure characteristics were measured using the calibrated instruments. Empirical prediction models were developed based on Random forest compared with the regression method. 46% of the workers were exposed to noise up to the exposure limit (85 dB for 8 h) and 54% of the workers were exposed to noise upper than it. It is observed that the considerable body response changes of workers exposed to noise from the medium to high levels (p < 0.05). Random forest could provide more accurate predictions than multiple regressions (R square = 0.73 to 0.80). Four variables as noise dose, noise sensitivity, age, and noise frequency are found to be the important factors influencing the psychological responses, respectively. Moreover, the main variables as noise sensitivity and noise dose and age are found to be the important factors influencing the physiological responses, respectively. Changes in the psychophysiological responses above the medium noise levels confirmed that the action level (82 dB for 8 h) can be a suitable criterion to prevent possible auditory and non-auditory complications. These dose-response models can be helpful in setting definitive exposure limits for noise-induced non-auditory effects at workplaces. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Noise & Vibration Worldwide SAGE

Empirical prediction model of psychophysiological responses of workers with respect to noise exposure based on random forest

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References (34)

Publisher
SAGE
Copyright
© The Author(s) 2022
ISSN
0957-4565
eISSN
2048-4062
DOI
10.1177/09574565221093258
Publisher site
See Article on Publisher Site

Abstract

Evidence of the non-auditory effects of noise exposure on workers is growing. This study aimed to empirically present prediction models of psychophysiological responses of workers with respect to noise exposure. In this study, 169 male workers from typical industrial workrooms were asked to judge the mental workload, noise annoyance, and noise sensitivity during exposure to noise. Two main physiological responses that include heart rate and blood pressure were also measured. Noise exposure characteristics were measured using the calibrated instruments. Empirical prediction models were developed based on Random forest compared with the regression method. 46% of the workers were exposed to noise up to the exposure limit (85 dB for 8 h) and 54% of the workers were exposed to noise upper than it. It is observed that the considerable body response changes of workers exposed to noise from the medium to high levels (p < 0.05). Random forest could provide more accurate predictions than multiple regressions (R square = 0.73 to 0.80). Four variables as noise dose, noise sensitivity, age, and noise frequency are found to be the important factors influencing the psychological responses, respectively. Moreover, the main variables as noise sensitivity and noise dose and age are found to be the important factors influencing the physiological responses, respectively. Changes in the psychophysiological responses above the medium noise levels confirmed that the action level (82 dB for 8 h) can be a suitable criterion to prevent possible auditory and non-auditory complications. These dose-response models can be helpful in setting definitive exposure limits for noise-induced non-auditory effects at workplaces.

Journal

Noise & Vibration Worldwide – SAGE

Published: Jun 1, 2022

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