Global ETD Search

1	Mitigating interference in Wireless Body Area Networks and harnessing big data for healthcare Jamthe, Anagha January 2015 (has links) No description available. Computer Science Wireless Body Area Networks Big Data in healthcare Interference mitigation IEEE 802 15 6 coexistence in WBANs sensor data analysis
2	Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities Cheng, Heng-Tze 01 December 2013 (has links) The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms. While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research. This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback. The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans. Activity recognition machine learning zero-shot learning active learning semantic attributes feature extraction probabilistic graphical models context-aware systems ubiquitous computing mobile computing user behavior modeling sensor data analysis artificial intelligence Electrical and Computer Engineering
3	Development of Novel Wearable Sensor System Capable of Measuring and Distinguishing Between Compression and Shear Forces for Biomedical Applications Dimitrija Dusko Pecoski (8797031) 21 June 2022 (has links) <p>There are no commercially available wearable shoe in-sole sensors that are capable of measuring and distinguishing between shear and compression forces. Companies have already developed shoe sensors that simply measure pressure and make general inferences on the collected data with elaborate software [2, 3, 4, 5]. Researchers have also attempted making sensors that are capable of measuring shear forces, but they are not well suited for biomedical applications [61, 62, 63, 64]. This work focuses on the development of a novel wearable sensor system that is capable of identifying and measuring shear and compression forces through the use of capacitive sensing. Custom hardware and software tools such as materials test systems and capacitive measurement systems were developed during this work. Numerous sensor prototypes were developed, characterized, and optimized during the scope of this project. Upon analysis of the data, the best capacitive measurement system developed in this work utilized the CAV444 IC chip, whereas the use of the Arduino-derived measurement system required data filtering using median and Butterworth zero phase low pass filters. The highest dielectric constant reported from optimization experiments yielded 9.7034 (+/- 0.0801 STD) through the use of 60.2% by weight calcium copper titanate and ReoFlex-60 silicone. The experiments suggest certain sensors developed in this work feasibly measure and distinguish between shear and compressional forces. Applications for such technology focus on improving quality of life in areas such as managing diabetic ulcer formation, preventing injuries, optimizing performance for athletes and military personnel, and augmenting the scope of motion capture in biomechanical studies.</p> Biomechanical engineering Biomedical instrumentation Medical devices Circuits and systems Microelectronics Composite and hybrid materials Wearables Wearable Sensors Sensors Biomedical Engineering Mechanical Engineering Electrical Engineering Biomechanics Disease Management Diabetic Neuropathy Ulcer Prevention Athlete Injury Prevention Human Performance Optimization Motion Capture Analysis Optimization Sensor Development Capacitive Sensing Sensor Performance Characterization Dielectric Materials Composites Measurement Systems Instrumentation Mechanical Testing Materials Testing System Sensor Design Sensor Materials Sensor Manufacturing Sensor Data Analysis Custom Hardware and Software Development Sensor Systems Capacitive Sensor Modeling Biomechanical Engineering Biomedical Instrumentation Circuits and Systems Composite and Hybrid Materials Medical Devices Microelectronics and Integrated Circuits

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