[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122204-en":3,"doc-seo-122204-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122204,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Implementation of Machine Learning for Analysis of an On Demand Passive Sweat Cortisol Sensor - Thesis","Cortisol is a steroid hormone that helps regulate the body’s stress response and can serve as a health biomarker linked to everyday habits. This work develops a noninvasive, passive sweat cortisol sensor using a flexible nano porous substrate, enabling on-demand testing integrated with wearable technologies. Sensor signals are measured via Electrochemical Impedance Spectroscopy (EIS) on synthetic sweat dosed with cortisol. Machine learning analyzes rising and falling cortisol trends over time using a weighted KNN approach, achieving 100% accuracy validated by k-means cross validation.","IMPLEMENTATION OF MACHINE LEARNING FOR ANALYSIS OF AN ON DEMAND  \nPASSIVE SWEAT CORTISOL SENSOR  \nby  \nSarah Abdel-Naser Shahub  \nAPPROVED BY SUPERVISORY COMMITTEE:  \nShalini Prasad, Chair  \nFang Bian  \nSoudeh Ardestani Khoubrouy  \nCopyright 2021  \nSarah Abdel-Naser Shahub  \nAll Rights Reserved  \nTo my family  \nIMPLEMENTATION OF MACHINE LEARNING FOR ANALYSIS OF AN ON DEMAND  \nPASSIVE SWEAT CORTISOL SENSOR  \nby  \nSARAH ABDEL-NASER SHAHUB, BS  \nTHESIS  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMASTER OF SCIENCE IN  \nBIOMEDICAL ENGINEERING  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nACKNOWLEDGMENTS  \nI would like to thank my supervising professor, Dr. Shalini Prasad, for her mentorship over this past year. She has been a great teacher and has taken the time to motivate me to further my understanding of the field of biosensors. Her emphasis on having a sound foundation of theory before putting knowledge into practice has helped to shape me as a researcher, and I am very thankful to her for her excellent guidance.  \nI would also like to thank Sayali Upasham, who has mentored me and supervised my research over the past year. She has from the start encouraged me towards my goal of completing my thesis and supported me throughout. I would also like to thank my colleagues Nathan Churcherand Cornelia Greyling, for helping me to prepare for my defense.  \nAugust 2021  \nIMPLEMENTATION OF MACHINE LEARNING FOR ANALYSIS OF AN ON DEMAND  \nPASSIVE SWEAT CORTISOL SENSOR  \nSarah Abdel-Naser Shahub, MS  \nThe University of Texas at Dallas, 2021  \nSupervising Professor: Shalini Prasad  \nCortisol is a steroid hormone produced by the adrenal glands for the purpose of regulating the body’s response to stress. Stress, as a physiological condition, can be caused by a wide variety offactors, such as mental exertion, diet, sleep, exercise, etc. For this reason, cortisol has the potential to serve as a biomarker for general health, as it relates to the everyday habits of patients. With the development of wearable technologies such as the smartwatch, increased attention has been focused on the development of noninvasive sensors for on demand testing that can integrated with wearable technologies. Current biosensing technologies for monitoring of chemical biomarkers such as cortisol depend on blood or salivary testing, which is invasive, costly, and time consuming. For this reason, the focus of this research is on the detection of cortisol through passive sweat, which contains many of the biomarkers present in blood at concentrations sufficient for detection. We have developed a noninvasive sensor on a flexible, nano porous substrate that has the capability to detect cortisol passively through sweat. The sensor data was then processed and input into a machine learning algorithm to analyze the rising and falling trend of cortisol concentration with time. The use of machine learning to analyze  \ncortisol trends can be used to inform the wearer of rising or falling cortisol levels, which can enable them to make informed decisions about their health and lifestyle. Sensor response was measured by conducting Electrochemical Impedance Spectroscopy (EIS) assays of synthetic sweat dosed with concentrations of cortisol within the physiological range, from which the responses for low, medium, and high concentrations of cortisol were found to be significant. Similar assays were performed within the frequency region of maximum capacitance with dosing regimens ranging from high to low and low to high concentrations of cortisol, to simulate the rise and fall of cortisol levels ofa human patient over a short period of time. The assay data was analyzed to find the rate of the change of the sensor response to a shift in cortisol concentration, which was then used to train a weighted KNN supervised machine learning algorithm to detect and classify increasing and decreasing cortisol concentrati","cbCaiaBk9MegRwjE","https://ap.wps.com/l/cbCaiaBk9MegRwjE","pdf",1248854,1,47,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# Chapter 1 Introduction\n# Chapter 2 Background\n# Chapter 3 Materials and Methods\n# Chapter 4 Results and Discussion\n# Chapter 5 Conclusion\n# References\n# Biographical Sketch\n# Curriculum Vitae","[{\"question\":\"Why is cortisol important for health monitoring?\",\"answer\":\"Cortisol is produced by the adrenal glands to regulate the body’s response to stress, and its level can reflect general health and everyday habits.\"},{\"question\":\"How does the proposed sensor detect cortisol?\",\"answer\":\"The sensor detects cortisol passively through sweat using a flexible nano porous substrate, with responses measured using Electrochemical Impedance Spectroscopy (EIS) assays on synthetic sweat.\"},{\"question\":\"How is machine learning used in this system?\",\"answer\":\"EIS assay data is processed to estimate the rate of change of sensor response as cortisol concentration shifts, then a weighted KNN supervised model classifies increasing versus decreasing cortisol trends.\"}]","Implementation of Machine Learning for Analysis of an On Demand Passive Sweat Cortisol Sensor - 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