CRII: CPS: Wearable-Machine Interface Architectures

Acknowledgment: This material is based upon work supported by the National Science Foundation under Grant Number 1755780

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Wrist-worn wearable devices provide rich sets of pulsatile physiological data under various modalities and circumstances. An unexploited capability is that the pulsatile physiological time series collected by wrist-worn wearable devices can be used for recovering internal brain dynamics. The goal of this project is to present wearable machine-interface (WMI) architectures related to mental stress and their potential applications for tracking fatigue and arousal states. Decoding brain states using wrist-worn wearables will transform how mental-stress-related diseases are diagnosed and treated. This proposal presents two design classes of WMI architectures related to mental stress: (1) A decoder that using skin conductance data recovers undesired stimuli triggered brain activity from wearables and a controller that delivers intermittent stimulation to reverse the adverse effect of stimuli. (2) A decoder that using cortisol data recovers undesired stimuli-triggered inhibition of cortisol secretion from wearables and a controller that delivers intermittent stimulation to generate the desired cortisol profile. The proposed methods will be validated by analyzing electrodermal activity as well as concurrent cortisol and adrenocorticotropic hormone pulsatile data in the context of mental-stress-related arousal and fatigue.

Selected Media Attention

Wearable Technology to Track Brain, Predict Illness, University of Houston Press Release, 2018.

Decoding The Brain: A Wrist-worn Wearable Designed To Fight Stress In The Age Of AnxietyFashNerd, a Digital Magazine, 2018.

Wearable technology to track brain, predict illness, EurekAlert, Science News, 2018.

Wearable Devices Explored to Detect Emotional States, Science & Enterprise, 2018.

Texas Researcher Granted Funds to Research Wearable Tech Applications in Mental HealthFyxes.com, Startup and Tech Stories, 2018.

Wearable Technology to Track Brain, Predict Illness, HealthNewsDigest, 2018.

Can I check your watch? I want to see what’s on your mind, MobiHealthNews, 2018.


 Amin M.R., and Faghih R.T., “Sparse Deconvolution of Electrodermal Activity via Continuous-Time System Identification” IEEE Transactions on Biomedical Engineering (2018).

This paper proposes a convex optimization scheme for deconvolution to overcome the non-convexity challenge in system parameter estimation of electrodermal activity. The method is proposed based on the continuous-system identification with modulating function technique with a novel adaptive frequency band selection method. (supplementary_info)


Amin M.R., and Faghih R.T., “Inferring Autonomic Nervous System Stimulation from Hand and Foot Skin Conductance Measurements,”  52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2018.

This paper proposes a state-space model relating skin conductance signals from different locations of the body with a single neural stimuli input from the autonomic nervous system. It also provides a concurrent deconvolution algorithm to recover the neural stimuli from the concurrent recordings of skin conductance data from hand and foot. The deconvolution results can be used as observations to decode mental stress.

Wickramasuriya D.S., Qi C., and Faghih R.T., “A State-Space Approach for Detecting Stress from Electrodermal Activity,” the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, HI, 2018.  [Video]

This paper relates mental stress to the probability that a skin conductance response appears and decodes the mental stress state. For example, using data from an experiment for studying driver stress, the stress state was decoded during relaxation as well as driving on different road conditions. This could, for instance, be applied to reduce driver stress by automatically playing calming music when stress is detected.

Provisional Patent

R. T. Faghih. Wearable Machine Interface System for Tracking and Controlling Mental Stress Related Brain Dynamics using Wrist-Worn Wearable Devices, 2018. US Patent App. 62/614,070. [Tech Summary]

Educational and Outreach Activities 

 Undergraduate Research Videos:

Cortisol Sensor Video, Research Experience for Undergraduates (REU) Student Video By Mikayla Tessmer (Summer 2018).

Skin Conductance Video, Undergraduate Research Video by Huy Hua and Zanne Solitz (Summer 2018).

Undergraduate Research Posters:

Tessmer M.K., Wickramasuriya D.S., and Faghih R.T., “Emotional Valence Classification at High Arousal Using Heartrate Variability and Respiration”. NSF REU Poster Session, University of Houston, Summer 2018.

Hua H., Amin M.R., and Faghih R.T., “A State-space Investigation of Cortisol Alterations in Chronic Fatigue Syndrome”. Undergraduate Research D
ay, University of Houston, Fall 2018.

Undergraduate Senior Design Project:  Wearable Device to Recognize Attentiveness in the Workplace



Skin Conductance based Attentiveness Monitor Video

Senior Design Project Members:

Nicholas Hermann, Senior Design Project Computer Engineering Undergraduate Student (senior), University of Houston (January-Decmber 2018)



Huy Hua, Senior Design Project Electrical Engineering Undergraduate Student (senior), University of Houston (January-December 2018)



Zanne Soliz, Senior Design Project Electrical Engineering Undergraduate Student (senior), University of Houston (January-Decmber 2018)



Christopher Theriot, Senior Design Project Computer Engineering Undergraduate Student (senior), University of Houston (January-Decmber 2018)



Web Outreach:

Computational Medicine Lab’s Youtube Channel

K-12 and College Level Mentor on Engineer Girl, a website by the National Academy of Engineering.

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