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 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.

[1]  Qixun Zhang,et al.  An Investigation of Decision Analytic Methodologies for Stress Identification , 2013, International Journal on Smart Sensing and Intelligent Systems.

[2]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[3]  Jennifer A. Healey,et al.  Wearable and automotive systems for affect recognition from physiology , 2000 .

[4]  Chuan Yi Tang,et al.  Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction , 2007, IEEE Transactions on NanoBioscience.

[5]  Lei Zhang,et al.  Hierarchical multiple sensor fusion using structurally learned Bayesian network , 2010, Wireless Health.

[6]  David G. Stork,et al.  Pattern Classification , 1973 .

[7]  D. Frank Hsu,et al.  Rank-Score Characteristics (RSC) Function and Cognitive Diversity , 2010, Brain Informatics.

[8]  Burtram C. Fielding,et al.  Scientific Research and Essays , 2013 .

[9]  Mykola Pechenizkiy,et al.  What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[10]  Lizawati Salahuddin,et al.  Detection of Acute Stress by Heart Rate Variability Using a Prototype Mobile ECG Sensor , 2006 .

[11]  YONG DENG,et al.  Combining Multiple Sensor Features for stress Detection using Combinatorial Fusion , 2012, J. Interconnect. Networks.

[12]  D. Frank Hsu,et al.  Combinatorial fusion with on-line learning algorithms , 2008, 2008 11th International Conference on Information Fusion.

[13]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[14]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[15]  D. Frank Hsu,et al.  Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems , 2006 .

[16]  Damian M. Lyons,et al.  Combining multiple scoring systems for target tracking using rank-score characteristics , 2009, Inf. Fusion.

[17]  Kemal Polat,et al.  A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems , 2009, Expert Syst. Appl..

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Frederic Angus.  Front-end Analog Pre-Processing for Real Time Psychophysiological Stress Measurements , 2007 .

[20]  Salvatore Ruggieri,et al.  Efficient C4.5 , 2002, IEEE Trans. Knowl. Data Eng..

[21]  Emil Jovanov,et al.  Stress monitoring using a distributed wireless intelligent sensor system. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[22]  Ahmet Akbas.  Evaluation of the physiological data indicating the dynamic stress level of drivers , 2011 .

[23]  Martin L. Griss,et al.  Activity-Aware Mental Stress Detection Using Physiological Sensors , 2010, MobiCASE.

[24]  D. Frank Hsu,et al.  Consensus Scoring Criteria for Improving Enrichment in Virtual Screening , 2005, J. Chem. Inf. Model..

[25]  D. Frank Hsu,et al.  Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval , 2005, Information Retrieval.

[26]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.