Sensor Feature Selection and Combination for Stress Identification Using Combinatorial Fusion

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 challen...

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Bibliographic Details
Published inInternational journal of advanced robotic systems Vol. 10; no. 8
Main Authors Deng, Yong, Wu, Zhonghai, Chu, Chao-Hsien, Zhang, Qixun, Hsu, D. Frank
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 06.08.2013
Sage Publications Ltd
SAGE Publishing
Subjects
Algorithms
Combinatorial analysis
Datasets
Driving conditions
Experiments
Feature extraction
Methods
Physiology
Sensors
Stress
Support vector machines
Wearable technology
Feature Selection
Combinatorial Fusion
Stress Identification
Sensor Fusion
Feature Fusion
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Summary: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.
ISSN:1729-8806
1729-8814
DOI:10.5772/56344