Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip

Area of Science:

• Biomedical Engineering
• Neuroscience
• Transportation Safety

Background:

• Driver drowsiness is a significant factor in global road fatalities.
• Existing detection methods often rely on steering wheel grip force.
• Electromyography (EMG) offers a potential alternative for monitoring driver state.

Purpose of the Study:

• To develop a driver drowsiness detection system using forearm muscle EMG signals.
• To investigate the relationship between muscle activity and drowsiness levels during driving.
• To evaluate the efficacy of microneedle electrodes (MNE) for EMG acquisition.

Main Methods:

• EMG signals were recorded from forearm muscles during a one-hour driving task.
• Frequency domain analysis (STFT, spectrogram) assessed signal changes.
• A novel algorithm detected drowsiness based on EMG signal magnitude reduction.
• EMG data was compared between MNE and Ag/AgCl wet electrodes.

Main Results:

• Driver drowsiness increased over time, correlating with decreased forearm muscle activity.
• EMG signal frequency components shifted towards lower frequencies during the driving task.
• The proposed algorithm effectively detected reduced muscle activity in real-time.
• MNE provided results comparable to traditional Ag/AgCl electrodes.

Conclusions:

• Forearm muscle EMG activity decreases with increasing driver drowsiness.
• The proposed EMG-based algorithm shows promise for real-time drowsiness detection.
• MNE is a viable option for EMG signal monitoring in driving scenarios.