ReBreathe: A Calibration Protocol that Improves Stress/Relax Classification by Relabeling Deep Breathing Relaxation Exercises Academic Article uri icon


  • 2015 IEEE. Training stress-prediction models is challenging due to the difficulty in reliably eliciting stress and relaxation responses in participants. For example, a task intended to elicit a relaxation response (e.g., deep breathing) can have the opposite effect depending on the participant's appraisal of and familiarity with the exercise. Including such instances in a training set undermines the accuracy of the resulting prediction model. This paper presents a technique, ReBreathe, to identify such instances based on respiratory patterns and determine their accurate stress/relax labels. We compared this relabeling approach against two labeling techniques: 1) nominal labels obtained from the experimental protocol and 2) labels obtained from subjective assessments. We then trained generalized estimating equation regression models to predict the resulting stress/relax labels from measures of heart rate variability and electrodermal activity. Training the model using protocol labels achieved a classification rate of 0.53 on participants not included in the training set. Relabeling the exercises based on each participant's subjective ratings increased classification rates but only marginally (0.61). In contrast, relabeling the exercises based on respiratory patterns increased classification rates to 0.88, or a four-fold reduction in error rates. These results illustrate the unreliability of protocol and subjective labels during stress/relax exercises and the potential benefits of ReBreathe.

published proceedings

  • IEEE Transactions on Affective Computing

author list (cited authors)

  • Ahmed, B., Khan, H. M., Choi, J., & Gutierrez-Osuna, R.

citation count

  • 10

complete list of authors

  • Ahmed, Beena||Khan, Hira Mujeeb||Choi, Jongyong||Gutierrez-Osuna, Ricardo

publication date

  • April 2016