Mobile Phone Use Driver Distraction Detection Based on MSaE of Multi-Modality Physiological Signals

Driver distraction, a major cause of traffic crashes, is reported to reduce driving performance and be detected with vehicle behavioral features. It also induces physiological responses. Time and frequency-domain features of physiological signals have been used to study distraction, but they are susceptible to residual noise and tend to overlook complexity. Moreover, the resampling problem arises while analyzing physiological signals at multiple time scales. This paper proposes a novel framework based on multiscale entropy on absolute time scales (MSaE) and bidirectional long short-term memory (BiLSTM) network to mine the distraction information in multi-modality physiological signals and detect distraction automatically. Firstly, an entropy-based resampling method is adopted to find the suitable downsampling rates of electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG). Then, calculating entropy with absolute time scales instead of relative time scales in a sliding window is utilized to explore the fluctuations of each signal while distraction. Afterward, ReliefF is selected from conventional feature selectors to identify the optimal feature set for each signal. Finally, BiLSTM with time dependency is designed to detect driver distraction with the selected feature set. The results illustrate significant distinctions in the MSaE of multiple physiological signals between normal and distracted driving. Additionally, MSaE, superior to traditional features, is selected as the most discriminative feature for each signal in distraction mining. Furthermore, the accuracy is further improved by about 8%, incorporating multi-modality features rather than vehicle behavioral features. This study indicates the potential of employing various signals to understand and detect driver distraction effectively.