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Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning
Eglė Butkevičiūtė1, Liepa Bikulčienė2, Aušra Žvironienė2
1Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania.
Healthcare (Basel, Switzerland)
|January 21, 2023
Summary
This study introduces a novel method using fuzzy logic and machine learning to assess employee work ability based on physiological data. The findings suggest this approach can effectively predict health conditions, aiding in workplace safety and employee wellbeing.
Area of Science:
- Occupational Health
- Biomedical Engineering
- Data Science
Background:
- A healthy lifestyle is crucial for preventing chronic diseases and premature death.
- Workplaces significantly influence employee physical activity and overall wellbeing.
- Existing health assessment methods rely on subjective data like interviews and questionnaires.
Purpose of the Study:
- To develop an objective work ability evaluation system.
- To assess employee physiological states (cardiovascular, muscular, neural).
- To improve workplace safety and prevent health issues.
Main Methods:
- Data transformation using fuzzy logic with varied membership functions and thresholds.
- Classification of physiological data into good, moderate, and poor health stages.
- Application of a three-part Random Forest machine learning model for system-specific analysis.
Main Results:
- High testing accuracies achieved: 93% for cardiovascular, 87% for muscular, and 73% for neural systems.
- The model successfully classified physiological states for work ability assessment.
- Demonstrated effectiveness of fuzzy logic and machine learning in health evaluation.
Conclusions:
- The proposed work ability evaluation process is a promising tool for health management.
- This method can aid in preventing workplace accidents and chronic fatigue.
- Objective physiological assessment can enhance employee wellbeing initiatives.
