Name
Abstract | ||
|---|---|---|
This paper presents the results of a study developing expert system to support stress recognition based on Artificial Neural Network (ANN). Developed ANN is trained using data from Physionet database and collected data from other researchers. The implemented system for stress recognition uses drivers ECG signal, Galvanic Skin Response and Respiration Rate as parameters. Developed neural network was validated with 77 samples. Samples are obtained from subjects using Pasco sensors in 7D cinemas. Out of 77 samples, in 71% of subjects higher level of stress is recognized, while 29% of subjects are classified as subjects with normal vital functions. An accuracy of 99% and specificity of 98% is obtained. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MECO.2016.7525765 | 2016 5th Mediterranean Conference on Embedded Computing (MECO) |
Keywords | DocType | ISSN |
stress,galvanic skin response,heart rate,respiration rate,artificial neural network,classification | Conference | 2377-5475 |
ISBN | Citations | PageRank |
978-1-5090-2223-6 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berina Alic | 1 | 2 | 0.78 |
| Dijana Sejdinović | 2 | 0 | 0.34 |
| Lejla Gurbeta | 3 | 3 | 1.52 |
| Almir Badnjevic | 4 | 10 | 9.40 |