Lightweight PPG-Based Drowsiness Detection: A Multi-Domain Feature Ablation and Computational Analysis

Abstract

Photoplethysmography (PPG) is an effective noninvasive approach to detect drowsiness and cognitive impairment. Given its compatibility with wearable and edge devices, this method is increasingly adopted in environments requiring continuous monitoring, such as driving and high-intensity workloads. However, existing studies lack a systematic evaluation of individual feature domain contributions and do not adequately address the feasibility of deployment on resource-constrained hardware. This study proposes a multi-domain feature extraction pipeline comprising four domains (time, frequency, wavelet, and nonlinear), coupled with a Random Forest classifier for drowsiness detection. A comprehensive ablation study on 15 feature combinations is conducted to analyze the trade-off between classification performance and computational cost. Experimental results indicate that the 3-domain combination (time, wavelet, and nonlinear) achieves the highest F1-score (0.601), while the full 4-domain configuration yields a higher recall rate (0.775), making it more suitable for safety-critical applications. In particular, computational cost analysis indicates that the feature extraction phase constitutes the primary computational bottleneck of the system. Specifically, extracting nonlinear features alone consumes 245.2 ms, approximately 500 times more than the model’s inference time (0.5 ms with a memory footprint of 0.3 MB). These findings highlight the critical role of feature domain selection in practical deployment. Consequently, this study provides an empirical foundation for designing reliable and resource-optimized drowsiness detection systems on edge hardware platforms.