Interpretable Deep Learning Models for Wearable Data in Sleep and Stress Analysis: Bridging the Gap between Predictive Accuracy and Explainability in Personalized Health Monitoring

Simple item page
dc.contributor.advisorMoshirpour, Mohammad
dc.contributor.advisorDuffett-Leger, Linda
dc.contributor.authorBarati, Ronak
dc.contributor.committeememberMoshirpour, Mohammad
dc.contributor.committeememberDuffett-Leger, Linda
dc.contributor.committeememberBarcomb, Ann
dc.contributor.committeememberSameet Deshpande, Gouri
dc.date2024
dc.date.accessioned2024-01-30T19:27:25Z
dc.date.available2024-01-30T19:27:25Z
dc.date.issued2024-01-26
dc.description.abstractThis study integrates wearable technology, machine learning, and personal health to analyze human sleep patterns and stress levels. It aims to understand the impact of daily activities and physiological metrics on individual well-being, utilizing a broad data set from various individuals. The research compiles three interrelated studies, offering a detailed view of personalized health monitoring and its potential for future applications. The first study utilizes LSTM networks, as well as RNN, complemented by Explainable AI, particularly LIME. This approach provides a deep dive into the rich, extensive data gathered from smartwatches, revealing how our daily routines—our steps, heart rates, stress, and physical activities—influence the sleep duration of our different levels of sleep Through this in-depth analysis, not only are we able to uncover the subtle but significant ways in which our lives influence our sleep, but the data allows us to develop tailored health interventions specific to everyone. The second study makes use of data from wearable devices to classify sleep levels using seven machine-learning models. Throughout this journey, stress plays a pivotal role in affecting sleep quality. The comparison of models with and without stress data suggests a compelling case for holistic health monitoring. An important finding of models that incorporate stress data is that psychological factors play a significant role in understanding and improving sleep health. The implications of this insight have a significant supporting on the development of wearable technologies and health monitoring systems, advancing our understanding of sleep disorders and treating them. In our final study, smartwatch data from first responders and their families were analyzed over three years using machine learning classifiers like SVM, Logistic Regression, KNN, Decision Trees, Random Forests, Naive Bayes, and XGBoost. The comparison between datasets with and without sleep data showed that sleep inclusion significantly boosts stress prediction accuracy to 98%, underlining the relationship between sleep and stress. This research offers vital stress management insights, especially for first responders.
dc.identifier.citationBarati, R. (2024). Interpretable deep learning models for wearable data in sleep and stress analysis: bridging the gap between predictive accuracy and explainability in personalized health monitoring (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/118141
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectExplainable AI
dc.subjectSleep
dc.subjectStress
dc.subject.classificationComputer Science
dc.subject.classificationEngineering--Biomedical
dc.titleInterpretable Deep Learning Models for Wearable Data in Sleep and Stress Analysis: Bridging the Gap between Predictive Accuracy and Explainability in Personalized Health Monitoring
dc.typemaster thesis
thesis.degree.disciplineEngineering – Electrical & Computer
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2024_barati_ronak.pdf
Size:
39.37 MB
Format:
Adobe Portable Document Format
Description:
I am planning to send part of my thesis to the IEEE Transaction on Biomedical Engineering: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10 The plan is for me to submit this work to the journal by February 15th at the latest. After that, I expect to hear back about reviews in a couple of months and hope to have the publication finalized within the requested period of 6 months.
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.64 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Restricted Theses and Dissertations