Szymon Fedor
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Szymon Fedor has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Akane Sano
Rice University
2 shared publications - Natasha Jaques
MIT Media Lab
2 shared publications - Rosalind Picard
MIT Media Lab, Cambridge, MA, USA
2 shared publications - Sara Taylor
Media Lab, Massachusetts Institute of Technology, Cambridge, MA
2 shared publications - Weixuan Chen
Empatica Boston Massachusetts USA
2 shared publications
Latest Publications
- Open Access
- 1 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Vol. 2015 (1094687X) , 6223-6226
Wavelet-based motion artifact removal for electrodermal activity
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.
- 1 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Vol. 2015 (1094687X) , 6223-6226
Open AccessWavelet-based motion artifact removal for electrodermal activity
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.
- Open Access
- 1 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2015 (1094687X) , 1934-1937
Automatic identification of artifacts in electrodermal activity data
Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.
- 1 August 2015
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2015 (1094687X) , 1934-1937
Open AccessAutomatic identification of artifacts in electrodermal activity data
Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.