Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7631
• Main Authors: Foster, Marc, Wu, Tianfu, Roberts, David L, Bozkurt, Alper
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: 3D printing; Adaptive filters; Analysis; Disasters; Dogs; Drones; ECG; Electrocardiogram; Electrocardiography; Electrodes; Evacuations & rescues; Gas detectors; Heart beat; Heart rate; heart rate variability; machine learning; Micro air vehicles (MAV); Monitoring; Physiology; Search and rescue operations; Sensors; Skin; Skin temperature; Software; Teams; wearable; United States > US
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197631

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.