WiHAR : From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition

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Use of Image-Based Machine Learning and Quantum Learning for Subsurface Characterization

The proposed thesis aims to explore novel applications of machine learning for subsurface characterization. In the first chapter, an image-based data-driven workflow is proposed to characterize oil viscosity from side-wall rock sample images. Informative features are extracted from the rock sample images deploying several image-based filters and statistical models. Both regression and classification tasks are performed on the preprocessed data. The proposed workflow shows promising results for viscosity classification whereas future work is needed to improve the regression performance. The second and third chapters explore the application of quantum-enhanced machine learning models for lithology classification and the resulting comparison with classical machine learning models. The second chapter compares a quantum support vector machine with a traditional support vector classifier for lithology classification from well log data. Different sample sizes are tested to understand if a quantum advantage is obtained when the available data is limited. The third chapter investigates the application of both quantum support vector and variational quantum classifier for binary lithology classification. The score distribution obtained from testing the models with multiple iterations gives more insight on the current performance capabilities of quantum-enhanced machine models when compared to artificial networks. Overall, although a quantum advantage is not observed in both chapters, this work opens the door to future applications of quantum-enhanced machine learning for subsurface characterization

A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

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Human Activity Recognition (HAR) Using Wearable Sensors and Machine Learning

Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer Interaction (HCI), rehabilitation engineering, occupational science, among others, resulting in significantly improved human safety and quality of life. Despite being an active research area for decades, HAR still faces challenges in terms of gesture complexity, computational cost on small devices, and energy consumption, as well as data annotation limitations. In this research, we investigate methods to sufficiently characterize and recognize complex human activities, with the aim to improving recognition accuracy, reducing computational cost and energy consumption, and creating a research-grade sensor data repository to advance research and collaboration. This research examines the feasibility of detecting natural human gestures in common daily activities. Specifically, we utilize smartwatch accelerometer sensor data and structured local context attributes and apply AI algorithms to determine the complex gesture activities of medication-taking, smoking, and eating. This dissertation is centered around modeling human activity and the application of machine learning techniques to implement automated detection of specific activities using accelerometer data from smartwatches. Our work stands out as the first in modeling human activity based on wearable sensors with a linguistic representation of grammar and syntax to derive clear semantics of complex activities whose alphabet comprises atomic activities. We apply machine learning to learn and predict complex human activities. We demonstrate the use of one of our unified models to recognize two activities using smartwatch: medication-taking and smoking. Another major part of this dissertation addresses the problem of HAR activity misalignment through edge-based computing at data origination points, leading to improved rapid data annotation, albeit with assumptions of subject fidelity in demarcating gesture start and end sections. Lastly, the dissertation describes a theoretical framework for the implementation of a library of shareable human activities. The results of this work can be applied in the implementation of a rich portal of usable human activity models, easily installable in handheld mobile devices such as phones or smart wearables to assist human agents in discerning daily living activities. This is akin to a social media of human gestures or capability models. The goal of such a framework is to domesticate the power of HAR into the hands of everyday users, as well as democratize the service to the public by enabling persons of special skills to share their skills or abilities through downloadable usable trained models