No abstract

Cortisol is a well established biomarker hormone that regulates many processes in the body and is widely referred to as the stress hormone. Cortisol can be used as a stress marker to allow for detection of stress levels in dogs during the training process. This test will indicate if they will handle the stress under the training or if they might be more suitable as an assistant or companion dog. An immunosensor for detection of cortisol was developed using electrochemical impedance spectroscopy (EIS). The sensor was characterized using chemical and topographical techniques. The sensor was calibrated and its sensitivity determined using a cortisol concentration range of 0.0005 to 50 μg/mL. The theoretical limit of detection was found to be 3.57 fg/mL. When the immunosensor was tested on canine saliva samples, cortisol was detected and measured within the relevant physiological ranges in dogs.

The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was developed for different behaviours. Three IMUs, each containing a 3-axis accelerometer, gyroscope, and magnetometer, were attached to the dogs’ chest, back, and neck. To build and test the model, data were collected during a video-recorded behaviour test where the trainee assistance dogs performed static postures (standing, sitting, lying down) and dynamic activities (walking, body shake). Advanced feature extraction techniques were employed for the first time in this field, including statistical, temporal, and spectral methods. The most important features for posture prediction were chosen using Select K Best with ANOVA F-value. The individual contributions of each IMU, sensor, and feature type were analysed using Select K Best scores and Random Forest feature importance. Results showed that the back and chest IMUs were more important than the neck IMU, and the accelerometers were more important than the gyroscopes. The addition of IMUs to the chest and back of dog harnesses is recommended to improve performance. Additionally, statistical and temporal feature domains were more important than spectral feature domains. Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. The best classifier achieved an f1-macro of 0.83 and an f1-weighted of 0.90 for the prediction of the five postures, demonstrating a better performance than previous studies. These results were attributed to the data collection methodology (number of subjects and observations, multiple IMUs, use of common working dog breeds) and novel machine learning techniques (advanced feature extraction, feature selection and modelling arrangements) employed. The dataset and code used are publicly available on Mendeley Data and GitHub, respectively.