Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

Classifying Livestock Grazing Behavior with the Use of a Low Cost GPS and Accelerometer

The ability to remotely track livestock through the use of GPS technology has tremendous potential to study livestock use patterns on the landscape. The use of high frequency accelerometers may give researchers and managers the ability to accurately partition GPS points into differing behaviors, giving further insight into livestock grazing selection, pasture use, and changes in forage preference through time. The objectives of this study were to 1) develop a classification algorithm to discriminate between graze and non-graze behaviors using a combination of metrics derived from a high frequency accelerometer motion sensor and a GPS data logger and 2) assess the accuracy of the classification algorithm using model error rates and expectant livestock behavior patterns

Behavioral Adaptations of Nursing Brangus Cows to Virtual Fencing: Insights from a Training Deployment Phase

Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries

Movement patterns, behavior, and habitat use of female moose on Joint Base Elmendorf-Richardson, AK

2016 Spring.Includes bibliographical references.Joint Base Elmendorf-Richardson (JBER), which is a combined United States Army/Air Force installation, and neighboring Anchorage, Alaska, support a population of moose Alces alces (Linnaeus, 1758) that inhabit a fragmented landscape of habitat types interspersed with human development. Because development plans in support of the military mission may have significant impacts on moose movement in the area, JBER and Alaska Department of Fish and Game (ADF&G) biologists began a study of moose habitat use and behavior on JBER. In order to help identify behaviors in wild radio-collared moose captured on JBER, we tested Telonics tri-axial accelerometers for accuracy in the detection of activity and the identification of behaviors in radio-collared moose. Direct observations of three captive animals fitted with radio collars containing accelerometers allowed us to calibrate activity readings to observed behaviors. We developed four datasets in order to test whether readings from this type of accelerometer could identify specific behaviors (browsing, grazing, walking, standing, lying), behavior categories (feeding, traveling, resting), or simply when moose were active or inactive. Multiple threshold criteria were tested in order to maximize correlation to observed behaviors. The highest overall accuracy was achieved when using threshold criteria to characterize behaviors as active (92.29% accuracy) or inactive (90.64% accuracy). A Fisher’s Exact Test indicated that there was no significant difference between observed behaviors and those correctly classified using threshold criteria for either active (p = .9728) or inactive (p = .9431) behaviors, indicating that our threshold criteria is correctly classifying these behaviors. In the next phase of this study, we collected 244,957 GPS locations from 18 female moose captured on JBER and fitted with GPS collars equipped with the same model tri-axial accelerometer used in the captive trials. Data from the accelerometers were used to characterize moose behavior as active or inactive. GPS locations, along with behavior patterns and movement characteristics, were used to rank JBER habitat types. Turning angle and speed were calculated between successive locations for each animal across the animal’s home range. Values were pooled for all animals and used to assess movement characteristics by season and habitat type. The highest velocity recorded for a 60 minute period was 1.50 m/s (5.40 kph), and 99.50% of all steps had velocities < 0.26 m/s (0.94 kph). Turning angle groups did not vary among either habitat types (p = 1.00) or seasons (p = 0.99). A new, intuitive home range estimation method, Dynamic Potential Path Area (dynPPA), was used to incorporate behavioral states into the delineation of animal home ranges. We delineated dynPPA home ranges by season for each moose, and used this technique in combination with Jacobs Index (which measures utilization in relation to availability) to determine habitat preference. Seasonal dynPPA home range sizes averaged 15.28 km2 in summer (SD = 6.43) and 23.25 km2 in winter (SD = 7.97). Habitat types most often used by moose on JBER included mixed deciduous/conifer (38.23% of summer locations and 30.03% of winter locations occurred within this habitat type), shrublands (15.04% of summer locations and 28.57% of winter locations), and deciduous forest (21.89% of summer locations and 19.08% of winter locations). While individual moose differed in habitat selection (F = 1.73, df = 17, p < 0.01), the most preferred habitat (according to Jacobs Index) on JBER in relation to its availability within the home range was shrublands

Evaluation of animal sensors and technology in grazing environments

The object of the first experiments were to determine the effects of virtual fencing on cortisol concentrations and behavior of beef cattle. Mixed breed beef heifers and cows (n = 55 and 59, respectively; initial BW = 315 ± 30 kg and 484 ± 84 kg, respectively) were randomly assigned to a physically fenced (PF) or virtually fenced (VF) pasture. Animals were rotated within respective treatments for 28 or 56 d, respectively. No significant differences were observed in animal behaviors, cortisol concentrations in hair or feces, nor lactate and non-esterified fatty acid concentrations. Virtual fencing was not more stressful to animals when compared to electric fencing. The objective of the second experiment was to validate the classification of the activities, and resource, terrace position, and burn unit usage of grazing cattle made by remote monitoring collars. Angus steers (n = 12; BW = 227 ± 45.0 kg) were fitted with an electronic GPS receiver and activity collar (Herd MOOnitor Ltd). Animal activities (collected every 4 s) were determined by a real time microcontroller and an algorithm for analyzing accelerometer data, and GPS locations (collected every 5 min) were collected and classified by the collar. Animal activities included grazing, walking, and resting. GPS locations included position on terraces, burn patch, and resource utilized. Data from the collars were matched to human observation data measuring the same activity and location parameters. Data from walking and resting activities, and resource and burn patch usage were accurately matched. However, grazing activity classification (≥30%) and terrace position accuracies (≥39%) were less than the reported NIR (≥39% and ≥42%, respectively), leading researchers to conclude that grazing activities could not be accurately classified

Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals

Classification of cattle behaviour in a forested habitat using data from activity sensors

GPS collars with activity sensors can be used to record movement and activity of free-ranging cattle. In forests grazed by cattle, resources are utilized where they exist, an important consideration for sustainable land use. Since animal behaviour can indicate state of health and nutrient uptake, monitoring grazing activity and classifying cattle behaviour based on collar recordings might contribute to provide sufficient welfare management. Several statistical methods have previously been trialled to classify behaviours using data from activity sensors, however, no method is standardised. This study mainly aims to use classification tree models to classify binary activity and grazing behaviour. 17 cattle on pasture in the forest of Stange and Romedal common land were equipped with dual-axis activity sensor collars and behaviour was observed during summer months of 2015, resulting in 1105 sequences of observed behaviour. Data from observations were used for testing accuracy of activity sensors to classify behaviour. Classification of binary activity (low vs. high) was 89.3%. When adding grazing as a category, classification was 80.8%. This suggests classification to be more difficult when adding more behaviour categories to the model and some behaviours are correlated to the same activity level. In addition to activity data from the collars, distance of movement between sequences was chosen by the model as an important variable to classify behaviour

Statistical interaction modeling of bovine herd behaviors

While there has been interest in modeling the group behavior of herds or flocks, much of this work has focused on simulating their collective spatial motion patterns which have not accounted for individuality in the herd and instead assume a homogenized role for all members or sub-groups of the herd. Animal behavior experts have noted that domestic animals exhibit behaviors that are indicative of social hierarchy: leader/follower type behaviors are present as well as dominance and subordination, aggression and rank order, and specific social affiliations may also exist. Both wild and domestic cattle are social species, and group behaviors are likely to be influenced by the expression of specific social interactions. In this paper, Global Positioning System coordinate fixes gathered from a herd of beef cows tracked in open fields over several days at a time are utilized to learn a model that focuses on the interactions within the herd as well as its overall movement. Using these data in this way explores the validity of existing group behavior models against actual herding behaviors. Domain knowledge, location geography and human observations, are utilized to explain the causes of these deviations from this idealized behavior