Multimodal Wearable Sensor Dataset for Driver Drowsiness Detection

This dataset was collected for research on driver drowsiness detection using multimodal, non-intrusive wearable sensors. It contains synchronized physiological and motion signals, including electrodermal activity (EDA), photoplethysmography (PPG), electrocardiography (ECG), and accelerometer data, recorded from participants under controlled experimental conditions. The dataset is intended to support the development and evaluation of algorithms for drowsiness and alertness estimation, multimodal sensor fusion, and wearable signal processing. By providing a low-cost and non-invasive data source, this dataset enables researchers to explore practical solutions for preventing road accidents and enhancing driver safety.