IJEP 44(9): 793-800 : Vol. 44 Issue. 9 (September 2024)
Shankar S.1,2*, Abass G.1, Rakesh Mohanty S.1, Sabarinathan A.1 and Radhakrishnan D.1
1. Kongu Engineering College, Department of Mechatronics Engineering, Erode – 638 052, Tamil Nadu, India
2. Nandha Engineering College, Department of Mechanical Engineering, Erode – 638 052, Tamil Nadu, India
Abstract
This research seeks to explore the potential link between occupational noise exposure and the development of mental stress in farmers while operating petrol-powered sprayers. The study employs an innovative approach utilizing the Emotiv EPOC neuro-headset. Agricultural tasks frequently involve the utilization of mechanized tools, such as petrol power sprayers, that are recognized for generating substantial levels of noise. Prolonged engagement with occupational noise has been previously correlated with a range of health consequences, including the manifestation of mental stress. The study’s methodology encompasses a thorough examination of noise discharge originating from frequently employed petrol power sprayers within agricultural environments. The Emotiv EPOC neuro-headset is employed to capture immediate neurophysiology information linked to stress reactions, thus furnishing valuable real-time data. For this, we ensured that the farmer wore the Emotiv EPOC headset in a secure manner, guaranteeing effective electrode contact with the scalp and directed the farmer to operate the petrol-power sprayer as typically would during their regular tasks. Through the results, we found that occupational noise levels of the sprayer are inversely proportional to stress levels. Specifically, when the noise level is below 60 dB(A), it corresponds to lower stress values, whereas noise levels surpassing 70 dB(A) are indicative of elevated stress levels.
Keywords
Emotiv EPOC neuro-headset, Farmers, Occupational noise exposure, Mental stress
References
- Hou, X., et al. 2015. EEG based stress monitoring. The 2015 IEEE International Conference on Systems, man and cybernetics (SMC2015). DOI: 10.1 109/SMC.2015.540.
- Wen, T.Y. and S.M. Aris. 2020. Electroencephalogram (EEG) stress analysis on alpha/beta ratio and theta/beta ratio. Indonesian J. Electr. Eng. Comput. Sci., 17(1): 175-182.
- Sulaiman, N., et al. 2018. Offline labview-based EEG signals analysis for human stress monitoring. The 2018 9th IEEE control and system graduate research colloquium (ICSGRC). DOI:10.1109/ICS GRC.2018.8657606.
- Jun, G. and K.G. Smitha. 2016. EEG based stress level identification. The 2016 IEEE International Conference on Systems, man and cybernetics (SMC2016). DOI: 10.1109/SMC.2016.7844738.
- Sciaraffa, N., et al. 2022. Validation of a light EEG-based measure for real-time stress monitoring during realistic driving. Brain Sci., 12(3): 304.
- Aspiotis, V., et al. 2022. Assessing electroence-phalography as a stress indicator: A VR high-altitude scenario monitored through EEG and ECG. Sensors. 22(15): 5792.
- Subramaniam, S., et al. 2022. Artificial intelligence technologies for forecasting air pollution and human health: A narrative review. Sustainability. 14 (16): 9951.
- Cao, H. and L. Peyrodie. 2023. Variational mode decomposition-based simultaneous R peak detection and noise suppression for automatic ECG analysis. IEEE Sensors J., 23(8): 8703-8713.
- Subramaniam, S., et al. 2023. Investigation of indoor air quality and pulmonary function status among power loom industry workers in Tamil Nadu, South India. Air Quality Atmos. Health. 2023: 1-16.
- Sharif, M.S., et al. 2023. An innovative random-forest-based model to assess the health impacts of regular commuting using non-invasive wearable sensors. Sensors. 23(6): 3274.
- Vijayasankar, A., et al. 2023. CNSD-net: Joint brain–heart disorders identification using remora optimization algorithm-based deep Q neural network. Soft Comput., 2023: 1-16.
- Li, Y., et al. 2023. Pilot stress detection through physiological signals using a transformer-based deep learning model. IEEE Sensors J., 23(11): 11774 – 11784.
- Attar, E.T. 2023. Integrated biosignal analysis to provide biomarkers for recognizing time perception difficulties. J. Medical Signals Sensors. 13(3): 217-223.
- Saedi, S., et al. 2022. Applications of electroence-phalography in construction. Automation Construction. 133: 103985.
- Sharma, S., G. Singh and M. Sharma. 2021. A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers Biol. Medicine. 134: 104450.
- Gaur, P., et al. 2021. A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI. IEEE Trans. Instrumentation Measure., 70: 1-9.
- Apicella, A., et al. 2021. EEG-based detection of emotional valence towards a reproducible measurement of emotions. Sci. Reports. 11(1): 21615.
- Arpaia, P., E. De Benedetto and L. Duraccio. 2021. Design, implementation and metrological characterization of a wearable, integrated AR-BCI hands-free system for health 4.0 monitoring. Measure., 177: 109280.
- Na, R., et al. 2021. An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator. Digital Signal Processing. 116: 103101.
- Apicella, A., et al. 2022. Enhancement of SSVEPs classification in BCI-based wearable instrumentation through machine learning techniques. IEEE Sensors J., 22(9): 9087-9094.
- Arpaia, P., et al. 2022. Performance enhancement of wearable instrumentation for AR-based SSVEP BCI. Measure., 196: 111188.
- Pei, Z., et al. 2020. EEG-based multiclass workload identification using feature fusion and selection. IEEE Trans. Instrumentation Measure., 70: 1-8.
- Du, G., et al. 2022. A multi-dimensional graph convolution network for EEG emotion recognition. IEEE Trans. Instrumentation Measure., 71: 1-11.
- Phutela, N., et al. 2022. Stress classification using brain signals based on LSTM network. Computational Intelligence Neurosci., DOI: 10.1155/2022/7607592.
- Goetz, C., et al. 2022. Industrial intelligence in the care of workers’ mental health: A review of status and challenges. Int. J. Ind. Ergonomics. 87: 103234.
- Paganelli, A.I., et al. 2022. Real-time data analysis in health monitoring systems: A comprehensive systematic literature review. J. Biomedical Inf., 127: 104009.
- Cannard, C., H. Wahbeh and A. Delorme. 2021. Electroencephalography correlates of well-being using a low-cost wearable system. Frontiers Human Neurosci., 15: 745135.
- Yan, W., et al. 2021. Research on the emotions based on brain-computer technology: A bibliometric analysis and research agenda. Frontiers Psychol., 12: 771591.
- Mansi, S.A., et al. 2021. Application of wearable EEG sensors for indoor thermal comfort measurements. Acta IMEKO. 10(4): 214-220.
- Hur, M., et al. 2010. Combination of statistical methods and Fourier transform ion cyclotron resonance mass spectrometry for more comprehensive, molecular-level interpretations of petroleum samples. Anal. Chem., 82(1): 211-218.
- Seixas, N.S., et al. 2012. 10-year prospective study of noise exposure and hearing damage among construction workers. Occup. Env. Medicine. 69(9): 643-650.
- Yun, S.M., et al. 2021. Recent advances in wearable devices for non-invasive sensing. Appl. Sci., 11(3): 1235.
- Ke, J., et al. 2021. Monitoring distraction of construction workers caused by noise using a wearable electroencephalography (EEG) device. Automation Constr., 125: 103598.