Comparison of Relationship between Accelerometer Outputs and Energy Expenditures in People with and without Down syndrome during Walking Activity

In recent years, there has been an increased interest in accurately measuring physical activity levels with accelerometers. Two distinct approaches have been used to estimate physical activity levels with accelerometers are vertical axis activity counts and vector magnitude (VM). Although previous studies evaluated these two distinct approaches for individuals without disabilities, employing VM may have a greater advantages for people with Down syndrome (DS) because of their unique movement pattern of increase movements along the mediolateral axis during walking. The purpose of this study was to identify which approaches, to physical activity monitoring, can better predict physical activity levels for people with and without DS while walking. A total of 37 participants completed the testing protocol, 18 participants with DS (age 19 – 64 years; 32.56 14.16) and 19 participants without DS (nDS; age 19 – 64 years; 31.61 12.90). All participants took part in one session of data collection involving walking at different speeds. Participants wore a GT3X+ triaxial accelerometer on their right hips to measure activity counts, a Oxycon Mobile System on the front of their body to measure energy expenditure, and a heart rate monitor to measure approximate relative intensity during testing protocols. All participants were asked to walk at three different speeds for six minutes at each speed of self-selected speed, slow speed (2 mph), and fast speed (4 mph) in a figure “8” shape with a five minute break between each trial. During the slow and fast speed trials, a trained pacer along with a calibrated wheel and speedometer walked in front of all participants to ensure maintenance of speed. The results showed the correlation between energy expenditure and accelerometer outputs, both vertical axis activity counts and VM for individuals without DS are 0.75 at a group levels using linear mixed effect models. And the correlation coefficient between energy expenditure and vertical axis activity counts and VM for people with DS are 0.53 and 0.64, respectively. There werer no significant difference between the correlations for the without DS group and a correlation approaching significance for the DS grou when comparing the correlation with energy expenditure between vertical axis activity counts and VM. Significant differences were found between groups when comparing correlation coefficients with energy expenditure and vertical axis activity counts using z – test (z = 1.99, p-value = 0.046). No significant difference was found between groups when comparing correlation coefficient between energy expenditure and VM (z = 1.06, p-value = 0.29). For people without DS, this study supported that using either approach yielded similar results. This result was surprising given the unique characteristics of people with DS. Additional studies are needed to continue to determine the accuracy of the accelerometer in measuring physical activity levels for people with DS accounting for their unique characteristic.Keywords: Intellectual Disability, Developmental Disability, Down syndrome, Physical Activity, Validity, Exercise, Accelerometers, Objective Measurements, Gai

Mobility classification of cattle with micro-Doppler radar

Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification. The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17). The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson’s Disease

It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms

Sensor-based approaches to monitoring the behaviour, health, and welfare of dairy calves

Currently, the detection of ill health in UK farmed calves is based on intermittent visual observation which is subjective and poorly accurate. Sensor-based monitoring may offer an improved alternative. For example, sensors could be used to monitor behaviour and detect signs of ill health in calves. However, substantial individual variation exists for many behaviours, the extent of which is poorly understood in calves. Here, within- and between- individual variation in calf feeding behaviours are quantified using data from computerised milk feeders. Results show that substantial, temporally stable individual differences exist. In addition, the average behavioural expression of two distinct feeding behaviours were positively and significantly correlated and the between-individual differences observed were shown to be consistent over time and context, and to be associated with weight gain. This improves our understanding of normal variation in calf feeding behaviour, which could be helpful in detecting potential behavioural changes indicative of ill health. Machine learning models were trained and tested using feeding data from computerised milk feeders to detect ill health. In a separate study, a similar methodology was used to detect ill health using reticulo-rumen temperature boluses. Results indicate low and moderate predictive performance, respectively. Study limitations and areas for future research are discussed. Finally, the development of novel technologies to enable a more holistic approach to behavioural monitoring in calves is explored. Results show that signals from a single collar-based sensor can be used to accurately detect nine different behaviours as well as to quantify rarely occurring behaviours, such as locomotor play. Quantifying play behaviour could provide a useful indicator of positive welfare in calves. It is also shown that these behaviours can be detected using computer vision, but that further work is needed to enable generalisation to new camera angles and scenes. Overall, this thesis highlights the potential of sensor-based technologies to improve our understanding of behavioural variation in calves, as well as to monitor a greatly more diverse range of behaviours than previously attempted. It is hoped that this work will contribute towards the improvement of health and welfare in calves

Physical Activity, Wellness and Health: Challenges, Benefits and Strategies

Regular physical activity (PA) is both a preventive measure and a cure for non-communicable diseases. Moreover, PA improves mental health, quality of life, and well-being. Conversely, physical inactivity and sedentary lifestyles have negative impacts on individuals, families, and society, as evidenced in particular by the spread of the obesity epidemic. PA has proven to be a low-cost alternative for the treatment and prevention of disease. Therefore, interventions to prevent avoidable diseases by increasing the proportion of physically active people are fundamental. The Special Issue “Physical Activity, Wellness and Health: Challenges, Benefits and Strategies” was collected research articles on anthropometric determinants of health and performance, PA and healthy habits, exercise and diet, exercise and body composition, interventions to promote PA for people of all ages, strategies for the implementation of an active life, and the beneficial effects of exercise on metabolic syndrome. A total of 20 articles were published, falling mainly into the following three areas: anthropometry, health, and sport; health benefits of exercise; population studies and strategies for an active life. All of the studies support strategies to promote PA and reduce sedentary behavior among adolescents, adults and the elderly. There is no doubt that regular exercise is beneficial to health, but the general population should be encouraged to engage in more of it

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Towards developing support tools for sustainable control of gastrointestinal nematodes in sheep : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Science at Massey University, Palmerston North, Manawatū, New Zealand

Gastrointestinal nematode (GIN) parasitism is a major animal health challenge for sheep. Parasitized animals typically display a number of clinical signs, including a reduction in voluntary feed intake, altered grazing behaviour and lethargy. The aim of this thesis was to use remote sensing technologies to advance the development of a methodology where early changes in animal behaviour can be used to help identify sheep suffering ill effects of GIN parasitism, especially in a pre-clinical situation. It was hypothesised that lambs with even modest worm burdens will be less active, graze for less time and spend more time resting than those herd mates that were less heavily parasitized. The movement and behavioural activity of young and mature, infected and uninfected sheep were monitored in a series of studies using global positioning system (GPS) and tri-axial accelerometer sensors. Key behaviours were identified using machine learning techniques. Also assessed was the influence of host genotype on movement activity. Accelerometry data accurately identified grazing, resting and walking activities of sheep. The sensors were able to identify the effects of GIN parasitism on movement and behaviour in sheep. Clear evidence was found that GIN were associated with reduced movement and overall activity in growing lambs, with reductions in time spent ‘grazing’ and ‘walking’ occurring concomitantly with increases in ‘resting’ activity, and before effects were recorded on growth rates. Host genotype also had an effect on movement activity of lambs in untreated sheep, but not in treated individuals. Adult sheep, however, showed no consistent changes in movement and behaviour associated with parasitism, as measured by faecal egg counts. Overall, the findings in this thesis have demonstrated the potential value in remote monitoring of sheep as a diagnostic marker to detect the generally subtle behavioural changes associated with changing GIN infection status. Such monitoring could therefore be used as the basis for deciding whether animals need to be treated with anthelmintic on the basis of individual need, and such decisions could be taken early, i.e. before animals have failed to grow adequately or started to manifest more overt signs of clinical illness such as weight loss