EMG Signal Segmentation to Predict Driver’s Vigilance State

Road accidents are the serious matter and need to be taken care of by certain parties. Some causes of road accidents are due to the poor driver vigilance when most drivers show signs of fatigue and loss of vigilance during long and monotonous driving. Muscle fatigue develops due to changes in the efficiency of the nervous system that can be seen in the declining the performance. This study will present a brief explanation of different methods for processing and classifying the electromyogram (EMG) signal to estimate driver’s muscle fatigue. The signal was obtained by pairing the electrode to the bicep brachii for two hours. Before that, the subject will answer a set of questionnaires and the score will be calculated to determine whether the driver in non-fatigue or fatigue condition. Signals were filtered and undergone feature extraction method. Then, the extracted features were undergoing feature selection method. Finally, to evaluate the performance measure of the feature selection method and classify the driver’s condition, ANN was applied. From the results obtained, ANN performance using features that undergo feature selection method produce a better classification accuracy compared to the ANN performance without feature selection method. Apart from that, EMG Signal Segmentation (ESS) methods were applied. The filtered signals were partitioned into few segments in segmentation process. The two hours signals were split into 60 and 30 min long segments for the first methodology and second methodology respectively. From each segment, the features were analysed to contemplate the progressions of the features and based on result, the change of features shows that every subject yields different result because the different person has different muscle condition. As a conclusion, relevant parameters that useful for the development of human safety by assessing muscle fatigue during the driving task were found.