Using collar-worn sensors to forecast thermal strain in military working dogs

Abstract

Military working dogs (MWDs) are at high risk of heat strain both during training and missions. Body heat in a MWD increases due to work, and the primary means for reducing this heat are resting and panting. Body-worn sensors can enable monitoring of work level and respiratory rate in real time. They can thereby provide real-time objective indicators of thermal strain in MWDs. In this paper a system is proposed for using collar-worn accelerometer, global positioning system (GPS), and audio recorder sensors to provide real-time estimates of work level and respiration (breathing and panting) rate. Automated methods are demonstrated for using a collar-worn accelerometer and GPS sensor to estimate work levels during multiple short-duration activities, and for estimating respiration rates from a collar-worn audio recorder. The potential utility of these estimates for forecasting and monitoring thermal strain is assessed based on performance in out of sample prediction of core temperature (Tc) statistics, which are obtained from ingestible sensors. Using cross-validation, regression models are trained from accelerometer- and GPS-based activity estimates to predict rate of change in Tc, obtaining a correlation of r=0.59 between actual and predicted Tc change rates. Regression models are also trained from audio-based respiration rate estimates during recovery to predict the Tc values immediately prior to recovery, obtaining a correlation of r=0.49 between actual and predicted Tc.