YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
The aim of this paper is to improve the information gained by the most commonly applied fit-for-duty sleepiness test (Pupillographic Sleepiness test, PST) by using pattern recognition approaches. The pupil diameter based sleepiness detection is enriched by several new features and machine learning methods. Using all newly computed pupil diameter features we achieved on the two-class detection problem (moderate sleepiness vs. high sleepiness) an accuracy of 83.03% on participant-dependent data with a Random Forest classifier. This result suggested that the PST-standard feature set should be enriched by the here proposed enlarged feature set.