Biomarkers.

Area of Science:

• Neuroscience
• Computer Science
• Transportation Science

Background:

• Daily cognitive performance fluctuations are increasingly measured but their impact on real-world behaviors like driving is unclear.
• Traditional linear models struggle to analyze complex driving data due to nonlinear relationships and temporal dependencies.
• This study introduces a novel hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to predict cognitive functioning from driving metrics.

Purpose of the Study:

• To assess the efficacy of a hybrid CNN-LSTM model in predicting cognitive functioning based on driving behavior metrics.
• To explore the relationship between daily cognitive variations and driving patterns.
• To develop a novel method for analyzing complex, sequential driving data.

Main Methods:

• Collected simultaneous data from smartphone-based cognitive tests (four times daily) and driving behavior metrics over months.
• Engineered features such as Distance Ratio and Short Trip Ratio, applied normalization and noise augmentation.
• Utilized a sliding window approach to create sequential data and trained a hybrid CNN-LSTM model with Conv1D and stacked LSTM layers.
• Employed Adam optimizer, Huber loss, and early stopping for model training and evaluation using MAE and R-squared.

Main Results:

• Feature analysis indicated weak but significant correlations for Distance Ratio (negative) and Short Trip Ratio (positive) with cognitive performance, with Distance Ratio being the most predictive.
• The CNN-LSTM model achieved high accuracy, with an R-squared of 0.9856 and Mean Absolute Error of 0.0321.
• Model predictions closely matched actual cognitive values, demonstrating strong generalization and robustness, with learning curves confirming no overfitting.

Conclusions:

• Hybrid CNN-LSTM models are effective in capturing nonlinear and temporal dependencies between cognitive functioning and driving behavior.
• Feature engineering and advanced modeling techniques significantly enhance predictive capabilities for sequential data.
• Future research can explore attention mechanisms and external factors (weather, traffic) to further improve predictive accuracy in applications like predictive analytics.