Int J Neural Syst - Detection of driving fatigue by using noncontact EMG and ECG signals measurement system.

Tópicos

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Resumo

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Resumo Limpo

driver fatigu can detect construct discrimin mode use featur obtain physiolog signal exist two major challeng kind method one collect physiolog signal subject drive without interrupt find featur physiolog signal correspond chang loss attent caus driver fatigu drive fatigu detect base studi surfac electromyographi emg electrocardiograph ecg drive period noncontact data acquisit system use collect physiolog signal bicep femori subject tackl first challeng fast independ compon analysi fastica digit filter util process origin signal base statist analysi result given kolmogorovsmirnov z test peak factor emg p maximum crossrel curv emg ecg p select combin characterist detect fatigu driver discrimin criterion fatigu obtain train sampl use mahalanobi distanc averag classif accuraci given fold crossvalid result show method propos paper can give well perform distinguish normal state fatigu state noncontact onboard vehicl driver fatigu detect system develop reduc fatiguerel risk

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