Stress Detection in Working People
| Autor: | S. Sriramprakash, O. V. Ramana Murthy, Vadana D. Prasanna |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Feature vector 010401 analytical chemistry Detector 020207 software engineering Pattern recognition 02 engineering and technology 01 natural sciences 0104 chemical sciences Stress (mechanics) Support vector machine Identification (information) ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Benchmark (computing) General Earth and Planetary Sciences Artificial intelligence business Skin conductance General Environmental Science |
| Zdroj: | Procedia Computer Science. 115:359-366 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2017.09.090 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|