Name
Abstract | ||
|---|---|---|
In this paper we address the problem of multimodal car driver stress recognition. To this aim, four different signals are considered: heart rate (HR), breathing rate (BR), palm EDA (P-EDA), and perinasal perspitation (PER-EDA). The raw signals are windowed and for each window 21 different features, including both time-domain and frequency-domain descriptors, are extracted. The recognition problem is formulated as a stress vs no-stress binary problem, and is addressed in two different experimental setups: five-fold cross validation and leave one subject out. In both setups the extracted features are classified, both individually and concatenated, with three different classifiers (k--NN, SVM, and ANN) using them both alone and stacking their predictions. Experiments run on a publicly available database of multimodal signals acquired in a controlled experiment on a driving simulator show that the best recognition results are obtained feeding the classifiers with the concatenation of the features of all the signals considered, reaching a micro average accuracy of 77.25% and 65.09% in the two experimental setups respectively.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3329189.3329221 | Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare |
Keywords | Field | DocType |
Driver fatigue, machine learning, stress detection | Driving simulator,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Concatenation,Controlled experiment,Stress recognition,Cross-validation,Distributed computing,Binary number | Conference |
ISSN | ISBN | Citations |
2153-1633 | 978-1-4503-6126-2 | 2 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Bianco | 1 | 226 | 24.48 |
| Paolo Napoletano | 2 | 339 | 37.19 |
| Raimondo Schettini | 3 | 1476 | 154.06 |