Combining Multiple Sensor Features For Stress Detection using Combinatorial Fusion
Physiological sensors have been used to detect different stress levels in order to improve human health and well-being. When analyzing these sensor data, sensor features are generated in the experiment and a subset of the features are selected and then combined using a host of informatics techniques (machine learning, data mining, or information fusion). Our previous work studied feature selection using correlation and diversity as well as feature combination using five methods C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, and k-Nearest Neighbors. In this paper, we use combinatorial fusion, based on performance criterion (CF-P) and cognitive diversity (CF-CD), to combine those multiple sensor features. Our results showed that: (a) sensor feature combination method is distinctly much better than CF-CD and other algorithms, and (b) CF-CD is as good as other five feature combination methods, and is better in most of the cases.
Cognitive diversity, combinatorial fusion, correlation, feature combination, feature selection, multiple scoring systems, rank-score characteristic (RSC) function, sensor fusion, stress identification
Computer Sciences | Numerical Analysis and Scientific Computing
Information Systems and Management
Journal of Interconnection Networks
World Scientific Publishing
DENG, Yong; HSU, D. F.; Wu, Z.; and CHU, Chao-Hsien.
Combining Multiple Sensor Features For Stress Detection using Combinatorial Fusion. (2012). Journal of Interconnection Networks. 13, (3/4), 1-17. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2237