There is a growing need for at-home respiration monitoring in canines, who are prone to respiratory issues due to breed-specific anatomy and active lifestyles. Continuous monitoring of respiration provides critical insight into stress and illness; however, current solutionsranging from clinical instruments to wearable devicesare either accurate but invasive and episodic or limited in fit and comfort. Here, we introduce a smart garment that integrates a spongy-like strain sensor and compact data acquisition module into commercially available canine apparel, enabling continuous, non-invasive monitoring of respiration, body temperature, and physical activity. Validation with two breeds, Labrador and Boxer, confirmed its ability to capture breed- and activity-specific respiration patterns, including differences in breathing rate, amplitude, and panting behavior. Using convolutional neural network (CNN)-assisted machine learning (ML), the system classified respiratory patterns across breeds and activity levels with over 94.3% accuracy. Beyond canines, this platform may hold potential for future adaptation to other companion animals, such as cats, suggesting a broader scope for home-based veterinary monitoring.
Hong et al. (Tue,) studied this question.