Remote vital sensing revolutionizes veterinary medicine with contactless technologies

Infrared thermography emerged as a proven modality for surface temperature measurement and early disease detection. It enables clinicians to visualize hotspots associated with infections, inflammation, and injury, such as mastitis in dairy cattle or musculoskeletal damage in horses, before clinical symptoms manifest. Its advantages are non-contact precision and real-time imaging, but limitations persist in environments with high humidity, air drafts, or obstructive fur and feather coverage.

A new study sheds light on how contactless remote vital sensing is reshaping animal healthcare by applying technologies like thermal cameras, special video systems, radar, wearable devices and computer analysis. Published in the journal Animals, the review "Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges, and Future Perspectives," explores how emerging sensing technologies are improving the early detection, monitoring, and welfare evaluation of animal patients across clinical, agricultural, and wildlife domains.

Primarily, the study investigates whether vital signs in animals can be measured reliably and continuously, without restraint, invasive probes, or direct physical contact. The research addresses a key gap in veterinary medicine: How can physiological data be gathered without elevating stress in animals or compromising accuracy?

The researchers assessed five core technologies: infrared thermography (IRT), remote photoplethysmography (rPPG), radar-based sensing, wearable sensors, and computer vision with machine learning. 

Infrared thermography emerged as a proven modality for surface temperature measurement and early disease detection. It enables clinicians to visualize hotspots associated with infections, inflammation, and injury, such as mastitis in dairy cattle or musculoskeletal damage in horses, before clinical symptoms manifest. Its advantages are non-contact precision and real-time imaging, but limitations persist in environments with high humidity, air drafts, or obstructive fur and feather coverage.

The review also explores whether deeper internal physiological signs could also be captured non-invasively through rPPG - a technique that measures heart and respiratory rates by analyzing subtle color fluctuations in the skin via camera recordings. While successful in dogs, cats, and even horses, challenges remain in signal extraction from animals with dense fur or unpredictable movements. Advances in machine learning, particularly convolutional neural networks and recurrent architectures, are boosting rPPG’s reliability across species, according to the study.

Radar-based sensing presents a contact-free avenue for continuous monitoring, using electromagnetic waves to detect micro-movements associated with breathing and heartbeat. This is especially applicable for large livestock and poultry, with studies demonstrating near 98% accuracy in respiration detection. Radar's ability to function across lighting conditions and penetrate fur adds unique value, though motion artifacts and environmental reflections pose unresolved challenges.

Next up, wearable sensors, although minimally invasive, are vital in continuous tracking applications. Devices include accelerometers, heart monitors, GPS, temperature loggers, and biochemical analyzers embedded in collars, harnesses, or patches. These are now widely used in companion animals and livestock, providing real-time alerts for anomalies. Nonetheless, battery life, comfort, species-specific fit, and ethical deployment in wildlife remain central concerns.

A major focus of the study was the integration of artificial intelligence and computer vision across all sensing modalities. AI significantly enhances data interpretation by isolating vital signs from noise, motion, or occlusion. For instance, thermal imaging paired with machine learning enables automatic diagnosis of lameness or stress in cattle. Algorithms now routinely outperform traditional veterinary methods in diagnostic accuracy. However, the paper warns of systemic bottlenecks: inconsistent species-specific datasets, limited model explainability, and the technical burden of implementation in rural or low-resource environments.

A key finding is that while these technologies hold transformative potential, their standalone use has limited scalability. The authors emphasize the need for multimodal systems - combining thermal imaging with rPPG, radar, and wearable data - to mitigate the weaknesses of any single approach. These systems must be powered by advanced signal processing and unified platforms capable of long-term monitoring and adaptive analysis.

The research also addresses critical structural and ethical questions: Can remote sensing systems be trusted across such a vast diversity of animal species? How should baseline thresholds for disease or stress be defined when individual physiology and environmental conditions vary so widely? And how can these tools be validated and regulated for clinical use?

Species diversity remains the foremost technical challenge. Veterinary patients differ widely in anatomy, skin properties, fur coverage, and behavior. Environmental interference, motion artifacts, and lack of standardization in data interpretation further complicate real-world deployment. While AI solutions can mitigate some variability through transfer learning and adaptive modeling, extensive calibration and validation remain essential for each use case.

Yet the study forecasts a promising trajectory. The development of species-adapted wearable designs, radar systems fine-tuned for livestock or wildlife, and miniaturized multi-sensor platforms is underway. Additionally, federated learning and digital health integration are allowing remote data collection to feed directly into veterinary electronic health records, laying the groundwork for precision medicine and population-wide surveillance.

The researchers envision remote sensing as a critical pillar in the One Digital Health framework - a global initiative unifying human, animal, and environmental health monitoring. With tools such as non-invasive thermography and AI-enabled video analysis, outbreaks in wildlife or zoonotic transmissions in farm settings could be flagged before they reach humans.

The study calls for coordinated efforts across veterinary science, bioengineering, and data science to ensure an ethical, affordable, and species-sensitive deployment of these technologies.