@conference{avimobicase2013,

title = {Chill-Out: Relaxation training through respiratory biofeedback in a mobile casual game},

author = {A Parnandi and B Ahmed and E Shipp and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avimobicase2013.pdf},

year  = {2013},

date = {2013-11-07},

booktitle = {Fifth International Conference on Mobile Computing, Applications and Services (MobiCASE 2013)},

keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{avipupilacii,

title = {Contactless Measurement of Heart Rate Variability from Fluctuations in Pupillary Dilation},

author = {A Parnandi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avipupilacii.pdf},

year  = {2013},

date = {2013-09-02},

booktitle = {Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)},

keywords = {Contactless sensing, Health, Heart rate variability, Stress},

pubstate = {published},

tppubtype = {conference}

}

@conference{avigameacii,

title = {A Control-Theoretic Approach to Adaptive Physiological Games},

author = {A Parnandi and Y Son and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avigameacii.pdf},

year  = {2013},

date = {2013-09-02},

booktitle = {Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)},

keywords = {Electrodermal activity, Games, Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{hira2013,

title = {Using an Ambulatory Stress Monitoring Device to Identify Relaxation Due to Untrained Deep Breathing},

author = {H Khan and B Ahmed and J Choi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/hira2013.pdf},

year  = {2013},

date = {2013-07-03},

booktitle = {35th Annual International Conference of the IEEE Engineering in Medicine & Biology Society},

pages = {n/a},

abstract = {We have developed a non-invasive stress monitoring device to analyze the physiological changes in a person while undergoing different tasks. Our main aim was to use the developed monitoring device to identify the efficacy of deep breathing as a relaxing activity in comparison to mentally stressful activities. A protocol with different mentally stressful activities intervened with regular sessions of deep breathing was designed. Participants were asked to perform the set of tasks in a lab setup. The data from three parameters i.e. heart rate, respiration and skin conductance was captured by the physiological sensors and was then analyzed. We found some very interesting results that subjects were not able to breathe properly and thus their stress level was high during the deep breathing exercise. Our results also validate the use of our device to accurately identify if the subjects were able to properly control their breathing to allow them to relax.},

keywords = {Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{masood2012embs,

title = {Consistency and Validity of Self-reporting Scores in Stress Measurement Surveys},

author = {K Masood and J Choi and B Ahmed and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/masood2012embs.pdf},

year  = {2012},

date = {2012-09-01},

booktitle = {2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)},

pages = {4895-4898},

abstract = {Stress has been attributed to physiological and psychological demands that exceed the natural regulatory capacity of a person. Chronic stress is not only a catalyst for diseases such as hypertension, diabetes, insomnia but may also lead to social problems such as marriage breakups, suicide and violence. Objective assessment of stress is difficult so self-reports are commonly used to indicate the severity of stress. However, empirical information on the validity of self-reports is limited. The present study investigated the authenticity and validity of different self-report surveys. An analysis, based on a three-pronged strategy, was performed on these surveys. It was concluded that although subjects are prone to systematic error in reporting, self-reports can provide a useful substitute for data modeling specifically in stress evaluation where other objective assessments such as determination of stress using only physiological response are difficult.},

keywords = {Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{alamudun2012phealth,

title = {Removal of Subject-Dependent and Activity-Dependent Variation in Physiological Measures of Stress},

author = {F Alamudun and J Choi and R Gutierrez-Osuna and H Khan and B Ahmed},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/alamudun2012phealth.pdf},

isbn = {978-1-936968-43-5},

year  = {2012},

date = {2012-05-21},

booktitle = {Proceedings of Pervasive Computing Technologies for Healthcare},

pages = {115},

abstract = {The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical activity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%.},

keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{choi2010sensorsc,

title = {Estimating mental stress using a wearable cardio-respiratory sensor},

author = {J Choi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2010sensorsc.pdf},

year  = {2010},

date = {2010-01-01},

booktitle = {Proceedings of IEEE Sensors},

pages = {150--154},

organization = {IEEE},

abstract = {This article describes a signal-processing approach to detect mental stress using unobtrusive wearable sensors. The approach addresses a major weakness of traditional methods based on heart-rate-variability (HRV) analysis: sensitivity to respiratory influences. To address this issue, we build a linear model that predicts the effect of breathing on the autonomic nervous system activation, as measured through HRV. Subtraction of respiratory effects leads to a residual signal that provides better discrimination between mental stress and relaxation conditions than traditional HRV tachogram. The method is experimentally validated on a discrimination task with two psycho-physiological conditions: mental stress and relaxation. To illustrate the effectiveness of the method, we impose a pacing respiratory signal that interferes with the main spectral band of the sympathetic branch. Our results suggest that the HRV residual signal has more discrimination power than conventional HRV analysis in the presence of respiration interferences.},

keywords = {Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}

@conference{choi2009using,

title = {Using heart rate monitors to detect mental stress},

author = {J Choi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2009using.pdf},

year  = {2009},

date = {2009-01-01},

booktitle = {Sixth International Workshop on Wearable and Implantable Body Sensor Networks},

pages = {219--223},

organization = {IEEE},

abstract = {This article describes an approach to detecting mental stress using unobtrusive wearable sensors. The approach relies on estimating the state of the autonomic nervous system from an analysis of heart rate variability. Namely, we use a non-linear system identification technique known as principal dynamic modes (PDM) to predict the activation level of the two autonomic branches: sympathetic (i.e. stress-inducing) and parasympathetic (i.e. relaxation-related). We validate the method on a discrimination problem with two psychophysiological conditions, one associated with mental tasks and one induced by relaxation exercises. Our results indicate that PDM features are more stable and less subject-dependent than spectral features, though the latter provide higher classification performance within subjects. When PDM and spectral features are combined, our system discriminates stressful events with a success rate of 83% within subjects (69% between subjects).},

keywords = {Stress, Wearable sensors},

pubstate = {published},

tppubtype = {conference}

}