Empirical study of muscle fatigue for driver’s ergonomic analysis during prolonged driving

Abstract

Driving has become essential in transporting people from one place to another. However, prolonged driving could cause muscle fatigue, leading to drowsiness and microsleep. Electromyography (EMG) is an important type of electro-psychological signal that is used to measure electrical activity in muscles. This work classifies and predicts muscle fatigue from trapezius muscle of 10 healthy subjects. The EMG signals and the time when muscle fatigue was experienced by the subjects were recorded. The mean frequency and median frequency of the EMG signals were extracted. For classification of muscle fatigue in non-fatigue and fatigue condition, six machine learning models were used: Logistic Regression, Support Vector Machine, Naïve Bayes, k-nearest Neighbour, Decision Tree and Random Forest. From the value of median frequency and slope coefficient of median frequency, mathematical model was developed with respect to driver’s physical factors. The results show that both the median and mean frequency are lower when fatigue conditions exist. In term of the classification performance, the highest accuracy for classifying muscle fatigue due to prolonged driving was obtained by the Random Forest classifier with 85.00%, using both the median and mean frequency of the EMG signals. This method of using the mean and median frequency will be useful in classifying driver’s non-fatigue and fatigue conditions and predict muscle fatigue during prolonged driving. The significant factor influencing muscle fatigue of the driver was Body Mass Index (BMI). This study successfully developed mathematical model of second order polynomial of muscle fatigue and BMI (p<0.05 and the R2 = 0.85). The model was successfully validated where the residual errors compared between predicted values and actual values were less than 10%.