Computer science graduate student runner-up for best paper award

Folami Alamudun, a Ph.D. student in the Department of Computer Science and Engineering, wasPhoto of Folami Alamudun runner-up for best paper award at thePervasive Health Conference held in San Diego May 21-24.

Wearable sensors make it now possible to measure non-invasively a number of physiological correlates of stress. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. Alamudun's paper, " Removal of subject-dependent and activity-dependent variation in physiological measures of stress," proposes two multivariate signal processing techniques to reduce the effect of both forms of interference. The two methods were validated on experimental data from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, the methods can reduce stress prediction errors by as much as 50 percent.

Alamudun received an undergraduate degree in electrical engineering from the University of Texas at El Paso in 2004. He is currently pursuing his Ph.D. in computer science under the supervision of Dr. Ricardo Gutierrez-Osuna. Alamudun's primary research interests include signal processing and wearable sensors for health applications. He is a member of the PRISM Lab.

Pervasive Health is a premier international forum with specific focus on technologies and human factors related to the use of ubiquitous computing in healthcare and well being.