Stress Detection in Working People

Stress detector classifies a stressed individual from a normal one by acquiring his/her physiological signals through appropriate sensors such as Electrocardiogram (ECG), Galvanic Skin Response (GSR) etc,. These signals are pre-processed to extract the desired features which depicts the stress level in working individuals. Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) are investigated to classify these extracted feature set. The result indicates feature vector with best features having a strong influence in stress identification. An attempt is made to determine the best feature set that results in maximum classification accuracy. Proposed techniques are applied on benchmark SWELL-KW dataset and state-of-art results are obtained.