(J-372) Using Wearable Brain Hemodynamic Monitoring Device to Prevent Workplace Injuries

Introduction::
Introduction




Workplace accidents represent a significant occupational health concern in labor-intensive industries, with 3.6% of workplace accidents leading to severe injuries or fatalities [1]. Existing workplace accident monitoring methods rely on subjective safety metrics and performance monitoring to identify mental fatigue and evaluate potential incidents [1]. Given the importance of occupational injury prevention preemptively, wearable technology is increasingly needed to monitor mental and physical fatigue to prevent workplace injuries [2].




Materials and Methods::
Materials and Methods




We explored the use of wearable near-infrared spectroscopy (NIRS) to detect mental fatigue and stress in simulated labor-intensive work conditions. NIRS is a non-invasive optical method for measuring cerebral hemodynamic changes, which can indicate a change in an individual's mental state [3]. The high stress and fatigue stages will be identified by analyzing the cerebral hemodynamic changes of participants under simulated work conditions. A customized NIRS probe was placed on the prefrontal region of the forehead, and participants were asked to perform metal grinding operations (Figure 1 A). Raw data of NIRS were collected and converted into activation intensity data using modified Beer-Lambert law [4].




Results, Conclusions, and Discussions::
Results and Discussion




The NIRS experiment data in Figure 1B shows fatigue and stress levels can be identified using wearable NIRS. The high-stress state is featured in different colors and confirmed by post-experiment interviews. The experiment results showed NIRS data can be a user-specific physiological parameter to predict physical and mental fatigue.


Conclusion




In conclusion, our study showed that wearable near-infrared spectroscopy could transform workplace safety by monitoring mental fatigue and stress levels in real-time and helping prevent workplace injuries and promoting a safer working environment across various industries.








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References

1. 
   Meredith, D., Mohammadrezaei, M., McNamara, J., & O’Hora, D. (2023). Towards a better understanding of farm fatalities: identification and estimation of farming fatality rates. Journal of agromedicine, 28(2), 239-253.
   
   

2. 
   Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2022). Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1), 2100099.
   
   

3. 
   Peng, Y., Li, C., Chen, Q., Zhu, Y., & Sun, L. (2022). Functional Connectivity Analysis and Detection of Mental Fatigue Induced by Different Tasks Using Functional Near-Infrared Spectroscopy. Frontiers in Neuroscience, 15, 771056.
   
   

4. 
   Kim, J., Hwang, W. S., Kim, D., & Kim, D. Y. (2022). Fast noniterative data analysis method for frequency-domain near-infrared spectroscopy with the microscopic Beer–Lambert law. Optics Communications, 520, 128417.