Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 41 for “"Human activity recognition"”.
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Human Activity Recognition using Hearing Aids
As people age, monitoring physical activity becomes increasingly important for assessing and maintaining health. Wearable devices are a promising means of accomplishing this measurement since they are unobtrusive, portable, and accessible. This thesis investigates the feasibility of using hearing …
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The role of representations in human activity recognition
… the role of representations in sensor based human activity recognition (HAR). In particular, we develop convolutional and recurrent autoencoder architectures for feature learning and compare their performance to a distribution-based representation as well as a supervised deep learning …
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Device-Free WiFi Sensing for Human Activity Recognition
Human activity recognition (HAR) using WiFi signals (WiFi-based HAR) has drawn considerable interest from the research community. In contrast to traditional device-based sensing techniques, WiFi-based HAR possesses several advantages, including convenience, wide availability, and privacy …
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Self-supervised Learning for IMU-based Human Activity Recognition
In recent years, human activity recognition has drawn considerable attention due to its application in a variety of areas such as smart homes and health. The pervasiveness of wearable devices and smartphones has provided many research opportunities for human activity recognition using inertial …
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Human activity recognition and gymnastics analysis through depth imagery
… many areas of computer vision, such as object recognition, human detection, human activity recognition, and sports analysis. The goal of my work is twofold: (1) use depth imagery to effectively analyze the pommel horse event in men’s gymnastics, and (2) explore and build upon the use of depth …
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Self-supervised learning for data-efficient human activity recognition
… into user behaviours. Within mobile sensing, human activity recognition is a fundamental task that aims to identify users' physical actions. Motivated by advancements in deep learning, human activity recognition research has also widely adopted these methods. However, compared to other data …
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NEURO-SYMBOLIC AI APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION
Sensor-based Human Activity Recognition (HAR) is an active research area, with relevant applications in healthcare and well-being. Deep Learning (DL) classifiers are currently the leading approach to tackle HAR, but their deployment is often limited by their inherent opacity and the scarcity of …
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COLLABORATIVE APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION IN DATA SCARCITY SCENARIOS
One of the most important goals of Human Activity Recognition (HAR) is to automatically obtain information on the behaviors of the users to proactively assist them with their tasks. In the literature, the majority of physical activity recognition approaches rely on fully- supervised techniques to …
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HUMAN ACTIVITY RECOGNITION FROM EGOCENTRIC VIDEOS AND ROBUSTNESS ANALYSIS OF DEEP NEURAL NETWORKS
… has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing egocentric view, which is a …
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A survey of IMU based cross-modal transfer learning in human activity recognition
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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2022: A Computational Odyssey - Towards a Deeper Understanding of Clustering Streaming Human Activity Recognition Data
… of data streams being readily available. Human Activity Recognition (HAR) is one such example and understanding the hierarchy of human movements can have positive implications in elderly care, physiotherapeutic therapy, surveillance, and general healthcare, among others. However, the …
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Intelligent robotics with digital-twin alignment : semantic navigation, manipulation, planning, and human-to-robot action transformation
… navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware …
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Metrics for analytics and visualization of big data with applications to activity recognition
Activity recognition systems detect the hidden actions of an agent from sensor measurements made on the agents' actions and the environmental conditions. For such systems, metrics are important for both performance evaluation and visualization purposes. In this thesis, such metrics are developed …
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Kinect depth video compression for action recognition
… all evaluated using a classification metric for human activity recognition. The first scheme uses the idea of companding to pre-process the data prior to compressing it with a standard H.264 coder. The second scheme uses a standard H.264 coder and appends additional feature bits to the compressed …
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A user-guided personalization methodology for new smart homes
… in order to provide the relevant services. Human activity recognition is a well-known technique used to under-stand user behaviours and enables the smart home services to run automatically according to the human mind. Observing the pattern of resident’s daily tasks is a useful technique used …
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Fusion of non-visual and visual sensors for human tracking
"Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. …
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Multi-modal on-body sensing of human activities
… the working efficiency. Due to unhandy human-computer-interaction methods this progress does not always result in increased efficiency, for mobile workers in particular. Activity recognition based contextual computing attempts to balance this interaction deficiency. This work …
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Recognition and Classification of Aggressive Motion Using Smartwatches
… care providers to reduce re-occurrences of this activity. A wearable technology approach for human activity recognition was explored in this thesis to detect aggressive movements. This approach aims to provide a means to identify the person that initiated aggressive motion and to categorize the …
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A Smart Energy-Efficient Hybrid Gait Monitoring System
… low frequency, waste biomechanical energy of human motion into useful electrical energy to run small body-worn electronics. This has shown promising results in multiple applications such as self-powered motion and haptic sensing, self-charging micro-storage devices, neuromorphic computing, and …
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