DUI Detection From Gait Using a Multichannel 1DCNN-Attention-BiLSTM Framework

Alcohol intoxication increases Blood Alcohol Content (BAC) and impairs cognitive, motor, and psychomotor functions, and contributed to over 30% of motor vehicle fatalities in 2017. Traditional methods for detecting Driving Under Influence (DUI), such as breathalyzers and blood tests, are invasive, require external hardware, and are unsuitable for continuous monitoring. Passive approaches such as gait analysis from smartphone sensor data offer a non-invasive solution for detecting impairment and enhancing road safety. Prior work has explored traditional machine learning (ML) and some Deep Learning (DL) approaches but have limitations such as analyzing on handcrafted features and facing challenges such as class imbalance and gait variability. This paper proposes a novel DL framework for passive alcohol intoxication detection from smartphone accelerometer data. A subject-level pre-processing pipeline was employed to address inter- and intra-subject variability, including stratified splitting, low-pass filtering, sliding window segmentation, and random oversampling to mitigate class imbalance. We propose the Multichannel Hybrid 1D-CNN-Attention-BiLSTM (MC-Hybrid) model, which extracts short-term features via parallel 1D-CNNs, uses a self-attention mechanism to increase weights on predictive patterns, and utilizes a bidirectional LSTM to model temporal dependencies. Rigorous evaluation includes comparison to a comprehensive set of ML and DL baselines, investigation of multiple sensor data window sizes, attention types, and ablation studies. MC-Hybrid achieved 93% accuracy and an F1-score of 0.8653, outperforming the state-of-the-art by 9.5% and all baselines by 9.0%. Self-attention resulted in a 2% performance gain over other attention mechanisms, demonstrating its effectiveness in DUI detection. The proposed method could be a practical, non-invasive approach to detect alcohol impairment.