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.

Pretty good governance: balancing policy drivers and stakeholder interests in developing fisheries ecosystem plans

The central research question that this thesis addresses is whether there is a significant gap between fishery stakeholder values and the principles and policy goals implicit in an Ecosystem Approach to Fisheries Management (EAFM). The implications of such a gap for fisheries governance are explored. Furthermore an assessment is made of what may be practically achievable in the implementation of an EAFM in fisheries in general and in a case study fishery in particular. The research was mainly focused on a particular case study, the Celtic Sea Herring fishery and its management committee, the Celtic Sea Herring Management Advisory Committee (CSHMAC). The Celtic Sea Herring fishery exhibits many aspects of an EAFM and the fish stock has successfully recovered to healthy levels in the past 5 years. However there are increasing levels of governance related conflict within the fishery which threaten the future sustainability of the stock. Previous research on EAFM governance has tended to focus either on higher levels of EAFM governance or on individual behaviour but very little research has attempted to link the two spheres or explore the relationship between them. Two main themes within this study aimed to address this gap. The first was what role governance could play in facilitating EAFM implementation. The second theme concerned the degree of convergence between high-level EAFM goals and stakeholder values. The first method applied was governance benchmarking to analyse systemic risks to EAFM implementation. This found that there are no real EU or national level policies which provide stakeholders or managers with clear targets for EAFM implementation. The second method applied was the use of cognitive mapping to explore stakeholders understandings of the main ecological, economic and institutional driving forces in the Celtic Sea Herring fishery. The main finding from this was that a long-term outlook can and has been incentivised through a combination of policy drivers and participatory management. However the fundamental principle of EAFM, accounting for ecosystem linkages rather than target stocks was not reflected in stakeholders cognitive maps. This was confirmed in a prioritisation of stakeholders management priorities using Analytic Hierarchy Process which found that the overriding concern is for protection of target stock status but that wider ecosystem health was not a priority for most management participants. The conclusion reached is that moving to sustainable fisheries may be a more complex process than envisioned in much of the literature and may consist of two phases. The first phase is a transition to a long-term but still target stock focused approach. This achievable transition is mainly a strategic change, which can be incentivised by policies and supported by stakeholders. In the Celtic Sea Herring fishery, and an increasing number of global and European fisheries, such transitions have contributed to successful stock recoveries. The second phase however, implementation of an ecosystem approach, may present a greater challenge in terms of governability, as this research highlights some fundamental conflicts between stakeholder perceptions and values and those inherent in an EAFM. This phase may involve the setting aside of fish for non-valued ecosystem elements and will require either a pronounced mind-set and value change or some strong top-down policy incentives in order to succeed. Fisheries governance frameworks will need to carefully explore the most effective balance between such endogenous and exogenous solutions. This finding of low prioritisation of wider ecosystem elements has implications for rights based management within an ecosystem approach, regardless of whether those rights are individual or collective.

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

Environmental and biological characteristics of lagoons on the southwest coast of Ireland

Coastal lagoons are defined as shallow coastal water bodies partially separated from the adjacent sea by a restrictive barrier. Coastal lagoons are protected under Annex I of the European Habitats Directive (92/43/EEC). Lagoons are also considered to be “transitional water bodies” and are therefore included in the “register of protected areas” under the Water Framework Directive (2000/60/EC). Consequently, EU member states are required to establish monitoring plans and to regularly report on lagoon condition and conservation status. Irish lagoons are considered relatively rare and unusual because of their North Atlantic, macrotidal location on high energy coastlines and have received little attention. This work aimed to assess the physicochemical and ecological status of three lagoons, Cuskinny, Farranamanagh and Toormore, on the southwest coast of Ireland. Baseline salinity, nutrient and biological conditions were determined in order to provide reference conditions to detect perturbations, and to inform future maintenance of ecosystem health. Accumulation of organic matter is an increasing pressure in coastal lagoon habitats worldwide, often compounding existing eutrophication problems. This research also aimed to investigate the in situ decomposition process in a lagoon habitat together with exploring the associated invertebrate assemblages. Re-classification of the lagoons, under the guidelines of the Venice system for the classifications of marine waters according to salinity, was completed by taking spatial and temporal changes in salinity regimes into consideration. Based on the results of this study, Cuskinny, Farranamanagh and Toormore lagoons are now classified as mesohaline (5 ppt – 18 ppt), oligohaline (0.5 ppt – 5 ppt) and polyhaline (18 ppt – 30 ppt), respectively. Varying vertical, longitudinal and transverse salinity patterns were observed in the three lagoons. Strong correlations between salinity and cumulative rainfall highlighted the important role of precipitation in controlling the lagoon environment. Maximum effect of precipitation on the salinity of the lagoon was observed between four and fourteen days later depending on catchment area geology, indicating the uniqueness of each lagoon system. Seasonal nutrient patterns were evident in the lagoons. Nutrient concentrations were found to be reflective of the catchment area and the magnitude of the freshwater inflow. Assessment based on the Redfield molar ratio indicated a trend towards phosphorus, rather than nitrogen, limitation in Irish lagoons. Investigation of the decomposition process in Cuskinny Lagoon revealed that greatest biomass loss occurred in the winter season. Lowest biomass loss occurred in spring, possibly due to the high density of invertebrates feeding on the thick microbial layer rather than the decomposing litter. It has been reported that the decomposition of plant biomass is highest in the preferential distribution area of the plant species; however, no similar trend was observed in this study with the most active zones of decomposition varying spatially throughout the seasons. Macroinvertebrate analysis revealed low species diversity but high abundance, indicating the dominance of a small number of species. Invertebrate assemblages within the lagoon varied significantly from communities in the adjacent freshwater or marine environments. Although carried out in coastal lagoons on the southwest coast of Ireland, it is envisaged that the overall findings of this study have relevance throughout the entire island of Ireland and possibly to many North Atlantic coastal lagoon ecosystems elsewhere.

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.