Interpretable Deep Learning Models for Wearable Data in Sleep and Stress Analysis: Bridging the Gap between Predictive Accuracy and Explainability in Personalized Health Monitoring
Date
2024-01-26
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Abstract
This 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.
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Keywords
Deep Learning, Machine Learning, Explainable AI, Sleep, Stress
Citation
Barati, 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.