Sensor feature selection and combination for stress identification using combinatorial fusion

Yong Deng, Zhonghai Wu, Chao Hsien Chu, Qixun Zhang, D. Frank Hsu

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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.

Original languageEnglish (US)
Article number306
JournalInternational Journal of Advanced Robotic Systems
StatePublished - Aug 6 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Artificial Intelligence


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