Key result
Hybrid CNN-LSTM model using ECG and respiration achieves 91% accuracy in driver drowsiness classification.
Why the study?
The high number of fatal crashes caused by driver drowsiness highlights the need for reliable detection methods that accurately estimate multiple levels of drowsiness without intervening in driving.
A hybrid CNN-LSTM model using ECG and respiration signals can accurately classify driver drowsiness levels in a simulated environment.
Hypothesis-generating for ECG-based drowsiness detection; prospective real-world validation needed before any clinical use.
The high number of fatal crashes caused by driver drowsiness highlights the need for developing reliable drowsiness detection methods. An ideal driver drowsiness detection system should estimate multiple levels of drowsiness accurately without intervening in the driving task. This paper proposes a multi-level drowsiness detection system by a deep neural network-based classification system using a combination of electrocardiogram and respiration signals. The proposed method is based on a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for classifying drowsiness by concurrently using heart rate variability (HRV), power spectral density of HRV, and respiration rate signal as inputs. Two models, a CNN-based model and a hybrid CNN-LSTM-based model were used for multi-level classifications. The performance of the proposed method was evaluated on experimental data collected from 30 subjects in a simulated driving environment. The performance and the results of both models are presented and compared. The best performance for both three-level and five-level drowsiness classifications was achieved by the CNN-LSTM model. The results indicate that the three-level and five-level classifications of drowsiness can be achieved with 91 and 67% accuracy, respectively.
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Ebrahimian et al. (2022) studied Driver drowsiness (n=30). Hybrid CNN-LSTM model using ECG and respiration signals vs. CNN-based model was evaluated on Three-level and five-level drowsiness classification accuracy. A hybrid CNN-LSTM model using ECG and respiration signals achieved 91% accuracy for three-level and 67% accuracy for five-level driver drowsiness classification.
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