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Published on: June 16, 2021
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Evaluation of tractor driving vibration fatigue based on multiple physiological parameters
Ruitao Gao1, Huachao Yan1, Zhou Yang1,2
1College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China.
Plos One
|July 14, 2021
Summary
Tractor vibrations negatively impact driver health. This study monitored physiological signals to quantify fatigue, developing an 88.9% accurate artificial neural network model for predicting discomfort and optimizing tractor design.
Area of Science:
- Human-machine interaction
- Occupational health
- Biomedical engineering
Background:
- Tractor operations generate low-frequency vibrations, posing risks to driver health and comfort.
- These vibrations can resonate with human organs due to similar natural frequencies, potentially causing long-term health issues.
Purpose of the Study:
- To investigate the physiological effects of tractor vibrations on drivers.
- To develop a method for evaluating vibration comfort using physiological indicators.
- To establish a predictive model for driver discomfort.
Main Methods:
- Collected four physiological signals: surface electromyography, skin conductivity, skin temperature, and photoplethysmography.
- Analyzed signal features to understand changes related to vibration fatigue.
- Trained an artificial neural network model using collected physiological data.
Main Results:
- Increased driver fatigue correlated with decreased electromyography median frequency and skin temperature slope.
- Fatigue also led to increased skin conductivity and photoplethysmography signal mean values.
- The artificial neural network model achieved an 88.9% prediction accuracy for vibration discomfort.
Conclusions:
- Physiological signals effectively indicate human body's response to vibration and fatigue.
- The developed model accurately predicts discomfort, aiding in objective assessment.
- Findings support optimizing tractor design for improved driver comfort and health.
