@article{,

title = {A review of digital innovations for diet monitoring and precision nutrition},

author = {B. J. Mortazavi and R. Gutierrez-Osuna},

url = {https://journals.sagepub.com/doi/full/10.1177/19322968211041356

https://psi.engr.tamu.edu/wp-content/uploads/2023/11/digitalInnovations2023bobak.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2021-07-14},

journal = {Journal of Diabetes Science and Technology},

keywords = {Chemical sensors, Diabetes, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{dennis2021tac,

title = {Towards Participant-Independent Stress Detection Using Instrumented Peripherals},

author = {D. R. da C. Silva and Z. Wang and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/9361293

https://psi.engr.tamu.edu/wp-content/uploads/2023/11/keyboard2023dennis.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2021-02-05},

journal = {IEEE Transactions on Affective Computing},

keywords = {Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{darpit2022jdst,

title = {Detection of hypoglycemia and hyperglycemia using ECG and accelerometer based non-invasive wearable sensors},

author = {D. Dave and K. Vyas and K. Branan and S. McKay and D. J. DeSalvo and R. Gutierrez-Osuna and G. L. Cote and M. Erraguntla},

year  = {2022},

date = {2022-07-06},

journal = {Journal of Diabetes Science and Technology},

keywords = {Continuous glucose monitors, Diabetes, Wearable sensors},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{anurag2021aminoacids,

title = {Predicting the macronutrient composition of mixed meals from dietary biomarkers in blood},

author = {A. Das and B. Mortazavi and S. Sajjadi and T. Chaspari and L. E. Ruebush and N. E. Deutz and G. L. Cote and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/9645322

https://psi.engr.tamu.edu/wp-content/uploads/2021/12/jbhi-aminoacids-2021-preprint.pdf},

year  = {2022},

date = {2022-06-04},

urldate = {2021-12-05},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {26},

number = {6},

pages = {2726-2736},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{nitin2021digitalhealth,

title = {Evaluating the role of breathing guidance on game-based interventions for relaxation training},

author = {N. V. Chakravarthy and D. R. Da Cunha Silva and R. Gutierrez-Osuna},

url = {https://www.frontiersin.org/articles/10.3389/fdgth.2021.760268/abstract

https://psi.engr.tamu.edu/wp-content/uploads/2021/12/nitin2021frontiers.pdf},

year  = {2021},

date = {2021-11-18},

urldate = {2021-11-18},

journal = {Frontiers in Digital Health},

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

pubstate = {published},

tppubtype = {article}

}

@article{martin2021clinicalnutrition,

title = {Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal},

author = {M. Hagve and S. Y. Simbo and L. E. Ruebush and M. P. K. J. Engelen and R. Gutierrez-Osuna and B. J. Mortazavi and G. L. Cote and N. E. P. Deutz},

url = {https://www.sciencedirect.com/science/article/pii/S0261561421003502

https://psi.engr.tamu.edu/wp-content/uploads/2021/07/1-s2.0-S0261561421003502-main.pdf},

year  = {2021},

date = {2021-07-13},

journal = {Clinical Nutrition},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{parnandi2018taffc,

title = {Partial reinforcement in game biofeedback for relaxation training},

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

url = {https://ieeexplore.ieee.org/document/8400398

https://psi.engr.tamu.edu/wp-content/uploads/2021/02/08400398.pdf},

year  = {2021},

date = {2021-02-28},

journal = {IEEE Transactions on Affective Computing},

volume = {12},

number = {1},

pages = {141-153},

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

pubstate = {published},

tppubtype = {article}

}

@article{ahmed2018tac,

title = {Gaming away stress: Using biofeedback games to learn paced breathing},

author = {B Ahmed and M Zafar and R Rihawi and R Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/8319498},

year  = {2020},

date = {2020-12-02},

journal = {IEEE Transactions on Affective Computing},

volume = {11},

number = {3},

pages = {519-531},

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

pubstate = {published},

tppubtype = {article}

}

@article{zaman2019scientificdata,

title = {Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities},

author = {S. Zaman and A. Wesley and D. Rodrigo Da Cunha Silva and P. Buddharaju and F. Akbar and G. Gao and G. Mark and R. Gutierrez-Osuna and I. Pavlidis},

url = {https://www.nature.com/articles/s41597-019-0249-5

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/shaila2019scientificdata.pdf},

year  = {2020},

date = {2020-11-08},

journal = {Scientific Data},

volume = {6},

number = {1},

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

pubstate = {published},

tppubtype = {article}

}

@article{akbar2019sensors,

title = {An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work},

author = {F. Akbar and G. Mark and I. Pavlidis and R. Gutierrez-Osuna},

url = {https://www.mdpi.com/1424-8220/19/17/3766

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/akbar2019sensors.pdf},

year  = {2019},

date = {2019-08-30},

journal = {Sensors},

volume = {19},

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

pubstate = {published},

tppubtype = {article}

}

@article{parnandi21017tac,

title = {Visual Biofeedback and Game Adaptation in Relaxation Skill Transfer},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2019/06/avinash-2019-tac.pdf},

year  = {2019},

date = {2019-05-17},

journal = {IEEE Transactions on Affective Computing},

volume = {10},

number = {2},

pages = {276 - 289},

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

pubstate = {published},

tppubtype = {article}

}

@article{wang2018jbhi,

title = {BioPad: Leveraging off-the-Shelf Video Games for Stress Self-Regulation},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2018jbhi.pdf},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics, in press},

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

pubstate = {published},

tppubtype = {article}

}

@article{parnandi2017jbhi,

title = {Physiological modalities for relaxation skill transfer in biofeedback games},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avinash2017jbhi.pdf},

year  = {2017},

date = {2017-03-01},

journal = {Journal of Biomedical and Health Informatics},

volume = {in press},

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

pubstate = {published},

tppubtype = {article}

}

@article{ahmed2015taffc,

title = {ReBreathe: A calibration protocol that improves stress/relax classification by relabeling deep breathing relaxation exercises},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/ahmed2015taffc.pdf},

year  = {2015},

date = {2015-08-01},

journal = {IEEE Transactions on Affective Computing},

volume = {in press},

keywords = {Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{avinash2014jmui,

title = {A Comparative Study of Game Mechanics and Control Laws for an Adaptive Physiological Game},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avinash2014jmui.pdf},

year  = {2014},

date = {2014-04-29},

journal = {Journal on Multimodal User Interfaces},

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

pubstate = {published},

tppubtype = {article}

}

@article{choi2012titb,

title = {Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2012titb.pdf},

year  = {2012},

date = {2012-03-01},

journal = {IEEE Transactions on Information Technology in Biomedicine},

number = {99},

pages = {279	- 286},

publisher = {IEEE},

abstract = {Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.},

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

pubstate = {published},

tppubtype = {article}

}

@article{choi2011removal,

title = {Removal of respiratory influences from heart rate variability in stress monitoring},

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

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2011removal.pdf},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Sensors Journal},

volume = {11},

number = {11},

pages = {2649-2656},

publisher = {IEEE},

abstract = {This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of monitoring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate monitors and respiratory sensors was constructed. Subtraction of respiratory driven fluctuations in heart rate leads to a residual signal where the effects of mental stress become more salient. We experimentally validated the effectiveness of this method on a binary discrimination problem with two conditions: mental stress of subjects performing cognitive tasks and a relaxation condition. In the process, we also propose a normalization method that can be used to compensate for ventilation differences between paced and spontaneous breathing. Our results suggest that, by separating respiration influences, the residual HRV has more discrimination power than traditional HRV analysis for the purpose of monitoring mental stress/load.},

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

pubstate = {published},

tppubtype = {article}

}