Title

Sensor Feature Selection and Combination for Stress Identification Using Combinatorial Fusion

Publication Type

Journal Article

Publication Date

2013

Abstract

The identification of stressfulness under certain driving condition is an important issue for safety, security and health. Sensors and systems have been placed or implemented as wearable devices for drivers. Features are extracted from the data collected and combined to predict symptoms. The challenge is to select the feature set most relevant for stress. In this paper, we propose a feature selection method based on the performance and the diversity between two features. The feature sets selected are then combined using a combinatorial fusion. We also compare our results with other combination methods such as naïve Bayes, support vector machine, C4.5, linear discriminant function (LDF), and k-nearest neighbour (kNN). Our experimental results demonstrate that combinatorial fusion is an efficient approach for feature selection and feature combination. It can also improve the stress recognition rate.

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Information Systems and Management

Publication

International Journal of Advanced Robotic Systems

Volume

10

First Page

306

Last Page

313

ISSN

1729-8806

Identifier

10.5772/56344

Publisher

Intech

Additional URL

http://dx.doi.org/10.5772/56344