Driver stress recognition for smart transportation: Applying multiobjective genetic algorithm for improving fuzzy c-means clustering with reduced time and model complexity

Abstract

Driver stress can lead to traffic deaths and injuries which ultimately bring on world economic loss. Researchers are in full swing to develop various algorithms for driver stress recognition (DSR) to enable smart transportation. Existing methods have encountered various difficulties. To relieve the problems and improve the accuracy, a DSR algorithm has been developed using multiobjective genetic algorithm based fuzzy c-means clustering (MOGA-FCM). This algorithm adopts Electrocardiogram (ECG) signal as input which is further used to compute features. Analysis has been carried out to investigate feature selection. Attention is drawn to the reduction of time and model complexity of MOGA-FCM. To reduce the time complexity, hyper-grid scheme is introduced, and the storage of membership matrix is eliminated. To reduce the model complexity, one of the objectives of the multiobjective optimization problem is to minimize the number of clusters. Results indicate that the time complexity and model complexity have been reduced by 68 %, and a reduction of two clusters, respectively. MOGA-FCM achieves equivalent true negative rate (TNReq) and equivalent true positive rate (TPReq) of 89.9 % and 91.7 % respectively. It improves the average true negative rate by 9.8–25.3 % and average true positive rate by 9.5–26.6 % compared to traditional clustering methods. Compared to existing biometric signal-based, speech-based and image-based approaches, it improves the performance by at least 7.02 % and 12.9 % in TNReq and TPReq respectively. As a result, the proposed algorithm can give an accurate and immediate reminding to drivers to take a rest or calm down in order to prevent traffic accident and lower the risk of developing stress-related disease.