2021
|
Parnandi, A.; Gutierrez-Osuna, R. Partial reinforcement in game biofeedback for relaxation training Journal Article In: IEEE Transactions on Affective Computing, vol. 12, no. 1, pp. 141-153, 2021. @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}
}
|
2020
|
Zaman, S.; Wesley, A.; Silva, D. Rodrigo Da Cunha; Buddharaju, P.; Akbar, F.; Gao, G.; Mark, G.; Gutierrez-Osuna, R.; Pavlidis, I. Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities Journal Article In: Scientific Data, vol. 6, no. 1, 2020. @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}
}
|
2019
|
Akbar, F.; Mark, G.; Pavlidis, I.; Gutierrez-Osuna, R. An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work Journal Article In: Sensors, vol. 19, 2019. @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}
}
|
Parnandi, A; Gutierrez-Osuna, R Visual Biofeedback and Game Adaptation in Relaxation Skill Transfer Journal Article In: IEEE Transactions on Affective Computing, vol. 10, no. 2, pp. 276 - 289, 2019. @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}
}
|
Akbar, F.; Bayraktaroglu, A. E.; Gao, G.; Grover, T.; Mark, G.; Storer, K.; Silva, D. R. Da Cunha; Gutierrez-Osuna, R.; Wang, Z.; Buddharaju, P.; Jones, N. Cooper; Pavlidis, I.; Wesley, A.; S. Zaman, Email Makes You Sweat: Examining Email Interruptions and Stress with Thermal Imaging Proceedings Article In: Proc. CHI, 2019. @inproceedings{akbar-2019-hci,
title = {Email Makes You Sweat: Examining Email Interruptions and Stress with Thermal Imaging},
author = {F. Akbar and A. E. Bayraktaroglu and G. Gao and T. Grover and G. Mark and K. Storer and D. R. Da Cunha Silva and R. Gutierrez-Osuna and Z. Wang and P. Buddharaju and N. Cooper Jones and I. Pavlidis and A. Wesley and S. Zaman, },
url = {https://psi.engr.tamu.edu/wp-content/uploads/2019/06/paper668.pdf
https://dl.acm.org/citation.cfm?id=3300898},
year = {2019},
date = {2019-05-04},
booktitle = {Proc. CHI},
journal = {Proc. CHI 2019},
keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2018
|
Wang, Z; Parnandi, A; Gutierrez-Osuna, R BioPad: Leveraging off-the-Shelf Video Games for Stress Self-Regulation Journal Article In: IEEE Journal of Biomedical and Health Informatics, in press, 2018. @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}
}
|
2017
|
Hair, A; Gutierrez-Osuna, R Deep Breaths: An Internally- and Externally-Paced Deep Breathing Guide Workshop Proc. 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 2017. @workshop{hair2017acii,
title = {Deep Breaths: An Internally- and Externally-Paced Deep Breathing Guide},
author = {A Hair and R Gutierrez-Osuna },
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/hair2017acii.pdf},
year = {2017},
date = {2017-10-23},
booktitle = {Proc. 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)},
abstract = {Deep breathing is a simple and intuitive technique for reducing stress, but requires familiarity with breathing exercises and suitable breathing parameters. We present Deep Breaths, a mobile tool that allows users to experiment with various respiratory pacing signals in order to maximize relaxation. Deep Breaths provides a stationary (i.e., clock-based) pacing signal as well as an adaptive pacing signal that follows fluctuations in the user’s heart rate. Deep Breaths also provides real-time visualizations of various standard measures of relaxation. This demonstration aims to illustrate how our system can be used for relaxation training.},
keywords = {Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {workshop}
}
Deep breathing is a simple and intuitive technique for reducing stress, but requires familiarity with breathing exercises and suitable breathing parameters. We present Deep Breaths, a mobile tool that allows users to experiment with various respiratory pacing signals in order to maximize relaxation. Deep Breaths provides a stationary (i.e., clock-based) pacing signal as well as an adaptive pacing signal that follows fluctuations in the user’s heart rate. Deep Breaths also provides real-time visualizations of various standard measures of relaxation. This demonstration aims to illustrate how our system can be used for relaxation training. |
Parnandi, A; Gutierrez-Osuna, R Physiological modalities for relaxation skill transfer in biofeedback games Journal Article In: Journal of Biomedical and Health Informatics, vol. in press, 2017. @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}
}
|
2015
|
Bhandari, R; Parnandi, A; Shipp, E; Ahmed, B; Gutierrez-Osuna, R Music-based respiratory biofeedback in visually-demanding tasks Proceedings Article In: 15th International Conference on New Interfaces for Musical Expression (NIME), 2015. @inproceedings{Bhandari2015nime,
title = {Music-based respiratory biofeedback in visually-demanding tasks},
author = {R Bhandari and A Parnandi and E Shipp and B Ahmed and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/Bhandari2015nime.pdf},
year = {2015},
date = {2015-05-31},
urldate = {2015-05-31},
booktitle = {15th International Conference on New Interfaces for Musical Expression (NIME)},
keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2014
|
Al-Rihawi, R; Ahmed, B; Gutierrez-Osuna, R Dodging Stress With A Personalized Biofeedback Game Proceedings Article In: Proc. CHI-PLAY, 2014. @inproceedings{rami2014chiplay,
title = {Dodging Stress With A Personalized Biofeedback Game},
author = {R Al-Rihawi and B Ahmed and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rami2014chiplay.pdf},
year = {2014},
date = {2014-10-19},
booktitle = {Proc. CHI-PLAY},
keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Harris, J; Vance, S; Fernandes, O; Parnandi, A; Gutierrez-Osuna, R Sonic Respiration: Controlling Respiration Rate Through Auditory Biofeedback Proceedings Article In: Proc. ACM CHI Conference on Human Factors in Computing Systems (CHI 2014) Works-in-Progress, 2014. @inproceedings{harris2014chi,
title = {Sonic Respiration: Controlling Respiration Rate Through Auditory Biofeedback},
author = {J Harris and S Vance and O Fernandes and A Parnandi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/harris2014chi.pdf},
year = {2014},
date = {2014-04-26},
urldate = {2014-04-26},
booktitle = {Proc. ACM CHI Conference on Human Factors in Computing Systems (CHI 2014) Works-in-Progress},
volume = {in press},
keywords = {Games, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2013
|
Parnandi, A; Ahmed, B; Shipp, E; Gutierrez-Osuna, R Chill-Out: Relaxation training through respiratory biofeedback in a mobile casual game Conference Fifth International Conference on Mobile Computing, Applications and Services (MobiCASE 2013), 2013. @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}
}
|
Parnandi, A; Gutierrez-Osuna, R Contactless Measurement of Heart Rate Variability from Fluctuations in Pupillary Dilation Conference Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2013. @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}
}
|
2012
|
Parnandi, A; Gutierrez-Osuna, R Contactless Measurement of Heart Rate Variability from Pupillary Fluctuations Technical Report 2012. @techreport{parnandi2012techreport,
title = {Contactless Measurement of Heart Rate Variability from Pupillary Fluctuations},
author = {A Parnandi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/parnandi2012techreport.pdf},
year = {2012},
date = {2012-12-04},
abstract = {The ability to measure a person’s physiological parameters in a contactless fashion without attaching electrodes to the skin has tremendous potential in making healthcare delivery more efficient. In this paper, we present a proof-of-concept method for measuring one such vital parameter, heart rate variability (HRV), in a contactless fashion from the spontaneous pupillary fluctuations. Pupillary measurements are done using a remote eye tracker for imaging and an integro-differential algorithm for the segmentation of the pupil-iris boundary. We estimate HRV from energy distribution in the low frequency (LF) (0.04 to 0.15 Hz) and high frequency (HF) (0.15 to 0.4 Hz) bands of the power spectrum of the pupillary fluctuations. In our study, we noted statistically significant correlation between the estimated HRV and the ground truth measures under a range of breathing conditions and under different illumination levels. The high degree of agreement evident in our results suggests that pupillary fluctuations obtained in a contactless fashion can be a reliable indicator of HRV.},
keywords = {Contactless sensing, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {techreport}
}
The ability to measure a person’s physiological parameters in a contactless fashion without attaching electrodes to the skin has tremendous potential in making healthcare delivery more efficient. In this paper, we present a proof-of-concept method for measuring one such vital parameter, heart rate variability (HRV), in a contactless fashion from the spontaneous pupillary fluctuations. Pupillary measurements are done using a remote eye tracker for imaging and an integro-differential algorithm for the segmentation of the pupil-iris boundary. We estimate HRV from energy distribution in the low frequency (LF) (0.04 to 0.15 Hz) and high frequency (HF) (0.15 to 0.4 Hz) bands of the power spectrum of the pupillary fluctuations. In our study, we noted statistically significant correlation between the estimated HRV and the ground truth measures under a range of breathing conditions and under different illumination levels. The high degree of agreement evident in our results suggests that pupillary fluctuations obtained in a contactless fashion can be a reliable indicator of HRV. |
Alamudun, F; Choi, J; Gutierrez-Osuna, R; Khan, H; Ahmed, B Removal of Subject-Dependent and Activity-Dependent Variation in Physiological Measures of Stress Conference Proceedings of Pervasive Computing Technologies for Healthcare, 2012, ISBN: 978-1-936968-43-5. @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}
}
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%. |
Choi, J; Ahmed, B; Gutierrez-Osuna, R Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors Journal Article In: IEEE Transactions on Information Technology in Biomedicine, no. 99, pp. 279 - 286, 2012. @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}
}
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. |
2011
|
Choi, J; Gutierrez-Osuna, R Removal of respiratory influences from heart rate variability in stress monitoring Journal Article In: IEEE Sensors Journal, vol. 11, no. 11, pp. 2649-2656, 2011. @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}
}
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. |
2010
|
Choi, J; Ahmed, B; Gutierrez-Osuna, R Ambulatory Stress Monitoring with Minimally-Invasive Wearable Sensors Technical Report 2010. @techreport{choi10techreport,
title = {Ambulatory Stress Monitoring with Minimally-Invasive Wearable Sensors},
author = {J Choi and B Ahmed and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2010techreport.pdf},
year = {2010},
date = {2010-11-10},
abstract = {Chronic stress can have serious health consequences, and is a leading risk factor for heart diseases, diabetes, asthma and depression. This article presents a minimally-invasive and wireless wearable sensor platform that can be used to monitor a number of physiological variables known to correlate with stress. We discuss the system design and sensor selection, both of which were guided as a tradeoff between information content and wearability. The platform is thoroughly evaluated through a battery of tests that elicit mental workload and physical activity, as well as through subjective assessments of comfort. Our results indicate that the sensor system is responsive to three broad types of factors: mental workload, posture and physical activity. We also describe a system-identification method that improves detection of mental stress by removing respiratory influences on heart rate.},
keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},
pubstate = {published},
tppubtype = {techreport}
}
Chronic stress can have serious health consequences, and is a leading risk factor for heart diseases, diabetes, asthma and depression. This article presents a minimally-invasive and wireless wearable sensor platform that can be used to monitor a number of physiological variables known to correlate with stress. We discuss the system design and sensor selection, both of which were guided as a tradeoff between information content and wearability. The platform is thoroughly evaluated through a battery of tests that elicit mental workload and physical activity, as well as through subjective assessments of comfort. Our results indicate that the sensor system is responsive to three broad types of factors: mental workload, posture and physical activity. We also describe a system-identification method that improves detection of mental stress by removing respiratory influences on heart rate. |
Choi, J; Gutierrez-Osuna, R Estimating mental stress using a wearable cardio-respiratory sensor Conference Proceedings of IEEE Sensors, IEEE 2010. @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}
}
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. |