Design and implementation of smart driving system using context recognition system

In this paper, we develop a context recognition system that is based on multimodal biometric signals and applicable to smart driving system. The context recognition system includes a biometric analysis module that analyzes and recognizes human biometric signal patterns. The context recognition system can recognize a user's emotion and level of concentration by analyzing ECG (electrocardiogram) and EEG (electroencephalogram) patterns. To predict the concentration and stress status of the user, the electroencephalogram rendering system utilizes 5 signal values: MID_BETA, THETA, ALPHA, DELTA, and GAMMA. Also, electrocardiogram analysis system utilizes 5 basic signal values: P, Q, R, S, T wave. To recognize the user's electrocardiogram signal patterns, a deformation K-means-based EM algorithm was applied.