This paper details the development of a novel wireless heart rate sensing system for puppies in training as guide dogs. The system includes a harness with on-board electrocardiography (ECG) front-end circuit, inertial measurement unit and a micro-computer with wireless capability where the major research focus of this paper was on the ergonomic design and evaluation of the system on puppies. The first phase of our evaluation was performed on a Labrador Retriever between 12 to 26 weeks in age as a pilot study. The longitudinal weekly data collected revealed the expected trend of a decreasing average heart rate and increased heart rate variability as the age increased. In the second phase, we improved the system ergonomics for a larger scale deployment in a guide dog school (Guiding Eyes for the Blind (Guiding Eyes)) on seventy 7.5-weekold puppies (heart rate coverage average of 86.7%). The acquired ECG based heart rate data was used to predict the performance of puppies in Guiding Eyes’s temperament test. We used the data as an input to a machine learning model which predicted two Behavior Checklist (BCL) scores as determined by expert Guiding Eyes puppy evaluators with an accuracy above 90%.

Wearable electrocardiogram (ECG) sensors can detect dogs’ heartbeat signals and have proven useful in monitoring dogs’ welfare and predicting temperament scores in structured evaluations of potential guide dog puppies. Despite advances in the ergonomics, performance, and usability of ECG sensor technologies specifically designed for dogs, deploying those systems in the real world imposes challenges such as training human operators to ensure electrodes’ proper contact with the skin and, especially in the case of puppies, socialization to achieve comfort and reduce behavioral inhibition. Seismocardiogram signal is an alternate modality for heartbeat signals and is acquired using the Inertial Measurement Unit (IMU), which is commercially available, widely deployed, and does not require skin-contact. However, the extracted signals from IMU are subject to heavy influences from motion and other noise sources. In this paper, we present a method that enables extracting the similar physiological parameters ECG provides using easier-to-deploy IMU sensors. We propose and evaluate a machine learning framework that reconstructs ECG signals from IMU signals even under moderate to heavy movements. Our study investigated two artificial neural network architectures to overcome severe noise artifacts in the IMU signal resulting from dogs’ movements and environmental factors. The first architecture combines the attention mechanism and convolution layers to extract important features from the temporal IMU input. The second architecture adapts contrastive representation learning to the regression problem and learns a more effective embedding for the ECG reconstruction. The qualitative inspection and quantitative analysis based on F1 scores of the R-peak alignment demonstrate the effectiveness of the two proposed models in removing motion noises and reconstructing realistic ECG signals, achieving an F1 score of 0.72 in the best case compared to 0.29 from the baseline.

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

Training guide dogs for visually-impaired people is a resource-consuming task for guide dog schools. This task is further complicated by a dearth of capabilities to objectively measure and analyze candidate guide dogs’ temperaments as they are placed with volunteer raisers away from guide dog schools for months during the raising process. In this work, we demonstrate a preliminary data analysis workflow that is able to provide detailed information about candidate guide dogs’ day to day physical exercise levels and gait activities using objective environmental and behavioral data collected from a wearable collar-based Internet of Things device. We trained and tested machine learning models to analyze different gait types including walking, pacing, trotting and mixture of walk and trot. By analyzing data both spatially and temporally, a location and behavior summary for candidate dogs is generated to provide insight for guide dog training experts, so that they can more accurately and comprehensively evaluate the future success of the candidate. The preliminary analysis revealed movement patterns for different location types which reflected the behaviors of candidate guide dogs.