Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.

Exploring the physicochemical basis of cheese texture, rheology and functionality, with emphasis on cation-casein interactions

The physicochemical properties of cheese and milk gels are greatly influenced by molecular interactions between the casein proteins involving calcium. Novel experiments were designed to investigate the relationship between insoluble caseinbound cations and rheological properties of Cheddar cheese and rennet-induced milk gels. Cheddar cheese and rennet-induced milk gels were supplemented with Mg2+ or Sr2+ to compare their effects on their rheological properties to those previously reported in literature for Ca2+ supplementation. Sr2+ displayed behaviour similar to Ca2+ as observed by its ability to increase the rigidity of cheese and rennet milk gels and also decrease cheese meltability. Mg+2 had no influence on cheese rheological properties and was greatly inferior to Ca2+ and Sr2+ in its ability to increase rennet milk gel elasticity. Cheddar cheese was supplemented with the calcium-chelating salts trisodium citrate, disodium hydrogen phosphate or disodium EDTA, in an attempt to reduce the CCP content of cheese and thereby modify its rheological and functional properties. TSC and EDTA were successful in decreasing cheese CCP, whereas DSP caused an initial increase in CCP content. Cheddar cheese was supplemented with chlorides of iron, copper and zinc at salting to investigate the effects of concentrations of these elements in excess of those found innately or commonly in fortification studies, with emphasis on mineral equilibria changes and resultant alteration of rheological properties. Zinc addition was the only added metal that significantly influenced cheese rheological properties, leading to an increase in cheese rigidity and decreased cheese melt at elevated temperatures. Gum tragacanth was used as a fat-replacer in the manufacture of reduced-fat Cheddar cheese, in an attempt to improve the rheological, functional and sensory properties of reduced-fat Cheddar. Overall, the experimental work reported in this thesis generated new knowledge and theories about how casein-mineral interactions influence rheological properties of casein systems.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Studies on the texture, functionality, rheology and sensory properties of Cheddar and Mozzarella cheeses

The effect of fortification of skim milk powder and sodium caseinate on Cheddar cheeses was investigated. SMP fortification led to decreased moisture, increased yield, higher numbers of NSLAB and reduced proteolysis. The functional and texture properties were also affected by SMP addition and formed a harder, less meltable cheese than the control. NaCn fortification led to increased moisture, increased yield, decreased proteolysis and higher numbers of NSLAB. The functional and textural properties were affected by fortification with NaCn and formed a softer cheese that had similar or less melt than the control. Reducing the lactose:casein ratio of Mozzarella cheese by using ultrafiltration led to higher pH, lower insoluble calcium, lower lactose, galactose and lactic acid levels in the cheese. The texture and functional properties of the cheese was affected by varying the lactose:casein ratio and formed a harder cheese that had similar melt to the control later in ripening. The flavour and bake properties were also affected by decreased lactose:casein ratio; the cheeses had lower acid flavour and blister colour than the control cheese. Varying the ratio of αs1:β-casein in Cheddar cheese affected the texture and functionality of the cheese but did not affect insoluble calcium, proteolysis or pH. Increasing the ratio of αs1:β-casein led to cheese with lower meltability and higher hardness without adverse effects on flavour. Using camel chymosin in Mozzarella cheese instead of calf chymosin resulted in cheese with lower proteolysis, higher softening point, higher hardness and lower blister quantity. The texture and functional properties that determine the shelf life of Mozzarella were maintained for a longer ripening period than when using calf chymosin therefore increasing the window of functionality of Mozzarella. In summary, the results of the trials in this thesis show means of altering the texture, functional, rheology and sensory properties of Mozzarella and Cheddar cheeses.