Lameness detection in sheep through behavioural sensor data analysis

Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy, therefore the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling

Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep

Lameness can be described as painful erratic movements, which relate to a locomotor system and result in the animal deviating from its normal gait or posture. Lameness is considered one of the major health and welfare concerns for the sheep industry in the UK that leads to a substantial economic problem and causes a reduction in overall farm productivity. According to a report in 2013 by ADAS entitled ‘Economic Impact of Health and Welfare Issues in Beef, Cattle and Sheep in England’, each lame ewe costs £89.80 due to the decline in body condition, lambing percentage, growth rate, and reduced fertility. Thus, early lameness detection eliminates the negative impact of lameness and increase the chance of favourable outcome from treatment. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal behaviours or movements which relate to lameness. The aim of this thesis was to evaluate the feasibility and accessibility of a proposed data mining approach (SLDM) to detect the early signs of lameness in sheep via analysing the retrieved data from a mounted wearable motion sensor within a sheep’s neck collar through investigating the most cost effective factors that contribute to lameness detection within the whole data mining process including; sensor sampling rate, segmentation methods, window size, extracted features, feature selection methods, and applicable classification algorithm. Three classes are recognised for sheep while their walking throughout the data collection process (sound, mild, and severe lameness classes). The sheep data were collected using three different sensor applications (Sheep Tracker, SensoDuino, SensorLog) which collect sheep data movements at different sampling rates 10, 5, and 4 Hz. Various sensing data were retrieved in X,Y, and Z dimensions; however, only accelerometer, gyroscope, and orientation readings are considered in the current study. Four sheep datasets are aggregated each of which includes 31, 10, 18, and 7 sheep. The conducted work in this thesis evaluates the performance of ensemble classifiers (Bagging, Boosting, or RusBoosting) using three different validation methods (5-fold, 0.3 hold-out, and proposed one ‘Single Sheep Splitting’) in comparison to three sampling rates (10, 5, 4 Hz), two segmentation approaches (FNSW and FOSW), three feature selection methods (ReliefF, GA, and RF) and three window sizes (10, 7, 5 sec.). Promising results of lameness prediction accuracies are achieved over most of the combinations (3 sampling rates, two segmentation methods, 3 window sizes, 183 extracted features, 3 feature selection methods, 3 ensemble classification models, and 3 model validation methods). However, the highest accuracy is revealed by using the `Bagging ensemble classifier 88.92% with F-score of 87.7%, 91.1%, 88.2% for sound walking, mildly walking, and severely walking classes, respectively. The results are obtained using 5-fold cross-validation over a 10 sec.window for sheep data collected at 10 Hz sampling rate using only the accelerometer hardware sensor reading and calculated orientation readings. The number of features selected is 46 optimised by GA using CHAID tree as a fitness function. Conversely, the lowest prediction accuracy of 56.25% with F-score (63.4% sound walking, 51.9% mildly walking, 48.8% severely walking) is recorded when RusBoosting ensemble is applied using 5-fold cross-validation over a 10 sec.window for dataset collected at the 4 Hz. sampling rate. So, the major research findings recommend that 10 Hz sampling rate is adequate for collect sheep movements, while the best segmentation method is FOSW as 20% of data-points are shared between two successive windows. Whereas, the preferable number of data-points (sheep movements) to be pre-processed is around 100, which is obtained when a 10 sec.window size or 7 sec.window size is applied. Additionally, the 20 features selected by RF out of 183 features could reveal good accuracy results compared to the whole set of extracted features. Although that GA feature selection method has slower execution time than RF, competitive prediction accuracy could be achieved when the selected features by GA were fed to the classifier. Finally, the acceleration sensor data alone are capable of making the decision about the lame sheep. So no extra hardware sensors like Gyroscope is required for decision making; moreover, the orientation sensor features could be directly derived from Acc which contribute most to lameness detection. Since the most cost effective factors are identified in this research, the practice in the meanwhile could be applicable for farmers, stakeholders, and manufacturers as no available sensor to detect the lame sheep developed yet. Therefore, the multidisciplinary nature of the conducted research opens diverse paths towards applying further research studies to develop various data mining approaches and practical sensor kits to detect the early signs of sheep’s lameness for better farm productivity and sheep industry prosperity in the UK

Identifying Lameness Movements in Sheep via Sensor Data Analysis

Lameness is one of the most significant issues for sheep well-being in the UK. Climate changes bringing mild winters and wet summers create muddy soil that is a good place for the Dichelobacter nodosus bacteria to survive and transmit easily to the sheep foot and cause footrot; which is one of the common causes for sheep lameness in the UK. According to the report from ADAS (Vickers and Wright, 2013), each lame ewe costs approximately £89.80 because of the decline in its performance alongside the extra labour and treatment costs that are needed. Although lameness is an endemic disease, it could be controlled from being spread within the whole flock. Previously, lameness can be spotted by a trained veterinarian or experienced shepherd either using a visual Locomotion Scoring (LS) or Gait Scoring (GS) system. It was a very time-consuming approach, took many efforts, and tended to be subjective. Therefore, an objective method to monitor the flock via sensor technology has been developed to collect data about the behaviour or gait of the animal to be analysed. This research aimed to utilise sensor devices to detect the early signs of lameness by collecting the movement measurements of the mounted sensor around the sheep’s neck. The collected data were analysed to classify the sheep into the lame and sound classes via machine learning approaches. However, collecting data on behaviour to study the gait changes for the sake of lameness detection in sheep is not a straightforward procedure. Firstly, the data were collected from Lodge Farm, Moulton College in Northamptonshire from several sheep. The data measurements involved movements of three axes around the neck. The y-axis was positioned to correspond with surge movements (forwards and backwards), the x-axis with sway motion (right and left), and the z-axis with heave (up and down) as shown in the figure below. Then, the raw data such as acceleration and orientation were tested by a range of machine learning classifiers. The initial results indicated that decision tree was the best machine learning classifier for the sheep sensor-based data. Moreover, the orientation of the surge axis is the best indicator of early signs of lameness; in this context orientation means the value of the angle around the axis which is the roll angle (y axis-surge). The figure shows the deployment of a mobile sensor which is used as a prototype sensor in this ongoing study

Lameness detection in sheep via multi-data analysis of a wearable sensor

Lameness is one of the significant concerns in the sheep industry in the UK. The prior detection of the lame sheep will be expected to decrease the prevalence of lameness, eliminate the annual loss of the individual sheep, and increase the overall farm productivity. The movement measurements for an individual sheep was retrieved from a mounted sensor on its neck. The sensor readings will be analysed according to data mining algorithms to predict lameness in its early stage. This study aims to build an automatic model to enable the shepherd to react quickly to the better treatment for the lame sheep

Lossless Image Compression based on Predictive Coding and Bit Plane Slicing

In this paper, a simple lossless image compression method based on a combination between bit-plane slicing and adaptive predictive coding is adopted for compressing natural and medical images. The idea basically utilized the spatial domain efficiently after discarding the lowest order bits namely, exploiting only the highest order bits in which the most significant bit corresponds to last layer7 used adaptive predictive coding, while the other layers used run length coding. The test results leads to high system performance in which higher compression ratio achieves for lossless system that characterized by guaranty fully reconstruction. General Terms Bit-plane slicing along with adaptive predictive coding for lossless image compression