Optimal Time Segments for Stress Detection

摘要: Some response signals being modeled for humans over some time segments may not be relevant analysis and modeling. These could contribute to reducing the quality of patterns captured by models, inefficient processing impose huge demands on storage resources. This work proposes an approach search from human particularly, physiological physical recognize stress. The paper determine that were critical differentiate types text based A support vector machine (SVM) was used classify different features signals. SVM genetic algorithm (GA) hybrid is developed optimal stress detection (OTSSD). As well as optimizing segments, GA also dealt with hundreds have included redundant irrelevant features. Optimal in reading successfully found classifier showed improvement recognition when optimized used.