Contactless monitoring of heart and respiratory rate in anesthetized pigs using infrared thermography

Long-term continuous monitoring of arrhythmias in pigs with insertable cardiac monitors

Arrhythmia detection is essential when assessing the safety of novel drugs and therapies in preclinical studies. Many short-term arrhythmia monitoring methods exist, including non-invasive ECG and Holter. However, there are no reliable, long-term, non-invasive, or minimally invasive methods for cardiac arrhythmia follow-up in large animals that allows free movement with littermates. A long follow-up time is needed when estimating the impact of long-lasting drugs or therapies, such as gene therapy. We evaluated the feasibility and performance of insertable cardiac monitors (ICMs) in pigs for minimally invasive, long-term monitoring of cardiac arrhythmias that allows free movement and species-specific behavior. Multiple implantation sites were tested to assess signal quality. ICMs recognized reliably many different arrhythmias but failed to detect single extrasystoles. They also over-diagnosed T-waves, resulting in oversensing. Muscle activity and natural startles of the animals caused noise, leading to a heterogeneous signal requiring post-recording evaluation. In spite of these shortcomings, the ICMs showed to be very useful for minimally invasive long-term monitoring of cardiac rhythm in pigs.

Pharmacokinetics of gamma-hydroxybutyric acid in 6-week-old swine (Sus scrofa domesticus) after intravenous and oral administration

Sedative as well as protective effects during hypoxia have been described for gamma-hydroxybutyric acid (GHB). Six swine (Sus scrofa domesticus) of 6 weeks old were administered NaGHB at a dose of 500 mg/kg intravenously (IV) and 500 and 750 mg/kg orally (PO) in a triple cross-over design. Repeated blood sampling was performed to allow pharmacokinetic analysis of GHB. Whole blood concentration at time point 0 after IV administration was 1727.21 ± 280.73 μg/mL, with a volume of distribution of 339.45 ± 51.41 mL/kg and clearance of 164.94 ± 47.05 mL/(kg h). The mean peak plasma concentrations after PO administration were 326.57 ± 36.70 and 488.01 ± 154.62 μg/mL for 500 mg/kg and 750 mg/kg, respectively. These were recorded at 1.42 ± 0.72 and 1.58 ± 0.58 h after PO dose for GHB 500 mg/kg and 750 mg/kg, respectively. The elimination half-life for IV and PO 500 mg/kg and PO 750 mg/kg dose was respectively 1.33 ± 0.30, 1.16 ± 0.31 and 1.11 ± 0.33 h. The bioavailability (F) for PO administration was 45%. No clinical adverse effects were observed after PO administration. Deep sleep was seen in one animal after IV administration, other animals showed head pressing and ataxia.

Towards substitution of invasive telemetry: An integrated home cage concept for unobtrusive monitoring of objective physiological parameters in rodents

This study presents a novel concept for a smart home cage design, tools, and software used to monitor the physiological parameters of mice and rats in animal-based experiments. The proposed system focuses on monitoring key clinical parameters, including heart rate, respiratory rate, and body temperature, and can also assess activity and circadian rhythm. As the basis of the smart home cage system, an in-depth analysis of the requirements was performed, including camera positioning, imaging system types, resolution, frame rates, external illumination, video acquisition, data storage, and synchronization. Two different camera perspectives were considered, and specific camera models, including two near-infrared and two thermal cameras, were selected to meet the requirements. The developed specifications, hardware models, and software are freely available via GitHub. During the first testing phase, the system demonstrated the potential of extracting vital parameters such as respiratory and heart rate. This technology has the potential to reduce the need for implantable sensors while providing reliable and accurate physiological data, leading to refinement and improvement in laboratory animal care.

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning

Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observation of cow status because they physically occlude each other. In this study, we propose a method that can monitor the respiratory behavior of multiple cows. Initially, 4,000 manually labeled images were used to fine-tune the YOLACT (You Only Look At CoefficienTs) model for recognition and segmentation of multiple cows. Respiratory behavior in the resting state could better reflect their health status. Then, the specific resting states (lying resting, standing resting) of different cows were identified by fusing the convolutional neural network and bidirectional long and short-term memory algorithms. Finally, the corresponding detection algorithms (lying and standing resting) were used for respiratory behavior monitoring. The test results of 60 videos containing different interference factors indicated that the accuracy of respiratory behavior monitoring of multiple cows in 54 videos was >90.00%, and that of 4 videos was 100.00%. The average accuracy of the proposed method was 93.56%, and the mean absolute error and root mean square error were 3.42 and 3.74, respectively. Furthermore, the effectiveness of the method was analyzed for simultaneous monitoring of respiratory behavior of multiple cows under movement, occlusion disturbance, and behavioral changes. It was feasible to monitor the respiratory behavior of multiple cows based on the proposed algorithm. This study could provide an a priori technical basis for respiratory behavior monitoring and automatic diagnosis of respiratory-related diseases of multiple dairy cows based on biomedical engineering technology. In addition, it may stimulate researchers to develop robots with health-sensing functions that are oriented toward precision livestock farming.

Grimace scale, burrowing, and nest building for the assessment of post-surgical pain in mice and rats-A systematic review

Several studies suggested an informative value of behavioral and grimace scale parameters for the detection of pain. However, the robustness and reliability of the parameters as well as the current extent of implementation are still largely unknown. In this study, we aimed to systematically analyze the current evidence-base of grimace scale, burrowing, and nest building for the assessment of post-surgical pain in mice and rats. The following platforms were searched for relevant articles: PubMed, Embase via Ovid, and Web of Science. Only full peer-reviewed studies that describe the grimace scale, burrowing, and/or nest building as pain parameters in the post-surgical phase in mice and/or rats were included. Information about the study design, animal characteristics, intervention characteristics, and outcome measures was extracted from identified publications. In total, 74 papers were included in this review. The majority of studies have been conducted in young adult C57BL/6J mice and Sprague Dawley and Wistar rats. While there is an apparent lack of information about young animals, some studies that analyzed the grimace scale in aged rats were identified. The majority of studies focused on laparotomy-associated pain. Only limited information is available about other types of surgical interventions. While an impact of surgery and an influence of analgesia were rather consistently reported in studies focusing on grimace scales, the number of studies that assessed respective effects was rather low for nest building and burrowing. Moreover, controversial findings were evident for the impact of analgesics on post-surgical nest building activity. Regarding analgesia, a monotherapeutic approach was identified in the vast majority of studies with non-steroidal anti-inflammatory (NSAID) drugs and opioids being most commonly used. In conclusion, most evidence exists for grimace scales, which were more frequently used to assess post-surgical pain in rodents than the other behavioral parameters. However, our findings also point to relevant knowledge gaps concerning the post-surgical application in different strains, age levels, and following different surgical procedures. Future efforts are also necessary to directly compare the sensitivity and robustness of different readout parameters applied for the assessment of nest building and burrowing activities.

A Review of Monitoring Techniques for Livestock Respiration and Sounds

This article reviews the different techniques used to monitor the respiration and sounds of livestock. Livestock respiration is commonly assessed visually by observing abdomen fluctuation; however, the traditional methods are time consuming, subjective, being therefore impractical for large-scale operations and must rely on automation. Contact and non-contact technologies are used to automatically monitor respiration rate; contact technologies (e.g., accelerometers, pressure sensors, and thermistors) utilize sensors that are physically mounted on livestock while non-contact technologies (e.g., computer vision, thermography, and sound analysis) enable a non-invasive method of monitoring respiration. This work summarizes the advantages and disadvantages of contact and non-contact technologies and discusses the emerging role of non-contact sensors in automating monitoring for large-scale farming operations. This work is the first in-depth examination of automated monitoring technologies for livestock respiratory diseases; the findings and recommendations are important for livestock researchers and practitioners who can gain a better understanding of these different technologies, especially emerging non-contact sensing.

The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence

Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Infrared Thermographic Imaging of Chest Wall Perfusion in Patients Undergoing Coronary Artery Bypass Grafting

Coronary artery disease represents a leading cause of death worldwide, to which the coronary artery bypass graft (CABG) is the main method of treatment in advanced multiple vessel disease. The use of the internal mammary artery (IMA) as a graft insures an improved long-term survival, but impairment of chest wall perfusion often leads to surgical site infection and increased morbidity and mortality. Infrared thermography (IRT) has established itself in the past decades as a non-invasive diagnostic technique. The applications vary from veterinary to human medicine and from head to toe. In this study we used IRT in 42 patients receiving CABG to determine the changes in skin surface temperature preoperatively, two hours, 24 h and 6 days after surgery. The results showed a significant and independent drop of surface temperature 2 h after surgery on the whole surface of the chest wall, as well as a further reduction on the left side after harvesting the IMA. The temperature returned to normal after 24 h and remained so after 6 days. The study has shown that IRT is sufficiently sensitive to demonstrate the known, subtle reduction in chest wall perfusion associated with IMA harvesting.

Preclinical evaluation of noncontact vital signs monitoring using real-time IR-UWB radar and factors affecting its accuracy

Recently, noncontact vital sign monitors have attracted attention because of issues related to the transmission of contagious diseases. We developed a real-time vital sign monitor using impulse-radio ultrawideband (IR-UWB) radar with embedded processors and software; we then evaluated its accuracy in measuring heart rate (HR) and respiratory rate (RR) and investigated the factors affecting the accuracy of the radar-based measurements. In 50 patients visiting a cardiology clinic, HR and RR were measured using IR-UWB radar simultaneously with electrocardiography and capnometry. All patients underwent HR and RR measurements in 2 postures—supine and sitting—for 2 min each. There was a high agreement between the RR measured using radar and capnometry (concordance correlation coefficient [CCC] 0.925 [0.919–0.926]; upper and lower limits of agreement [LOA], − 2.21 and 3.90 breaths/min). The HR measured using radar was also in close agreement with the value measured using electrocardiography (CCC 0.749 [0.738–0.760]; upper and lower LOA, − 12.78 and 15.04 beats/min). Linear mixed effect models showed that the sitting position and an HR < 70 bpm were associated with an increase in the absolute biases of the HR, whereas the sitting position and an RR < 18 breaths/min were associated with an increase in the absolute biases of the RR. The IR-UWB radar sensor with embedded processors and software can measure the RR and HR in real time with high precision. The sitting position and a low RR or HR were associated with the accuracy of RR and HR measurement, respectively, using IR-UWB radar.

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Simple Summary

Contactless physiological monitoring can be important for animal health and well-being. The current study investigated whether heart rate in pigs can be extracted automatically from videos without disturbing the pig and showed that this was possible with 4.69 beats per minute in mean absolute error. The study also tested different body regions and found that the abdomen was a better region to measure heart rate from videos compared to the front leg or the neck. However, future studies are needed that include videos with different light conditions, different housing systems and multiple pigs to enable real-time on-farm monitoring of heart rate from videos.

Abstract

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE_3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE_3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting.

Infrared thermal imaging associated with pain in laboratory animals

The science of animal welfare has evolved over the years, and recent scientific advances have enhanced our comprehension of the neurological, physiological, and ethological mechanisms of diverse animal species. Currently, the study of the affective states (emotions) of nonhuman animals is attracting great scientific interest focused primarily on negative experiences such as pain, fear, and suffering, which animals experience in different stages of their lives or during scientific research. Studies underway today seek to establish methods of evaluation that can accurately measure pain and then develop effective treatments for it, because the techniques available up to now are not sufficiently precise. One innovative technology that has recently been incorporated into veterinary medicine for the specific purpose of studying pain in animals is called infrared thermography (IRT), a technique that works by detecting and measuring levels of thermal radiation at different points on the body’s surface with high sensitivity. Changes in IRT images are associated mainly with blood perfusion, which is modulated by the mechanisms of vasodilatation and vasoconstriction. IRT is an efficient, noninvasive method for evaluating and controlling pain, two critical aspects of animal welfare in biomedical research. The aim of the present review is to compile and analyze studies of infrared thermographic changes associated with pain in laboratory research involving animals.

Use of Different Cooling Methods in Pig Facilities to Alleviate the Effects of Heat Stress-A Review

An increase in the frequency of hot periods, which has been observed over the past decades, determines the novel approach to livestock facilities improvement. The effects of heat stress are revealed in disorders in physiological processes, impaired immunity, changes in behaviour and decreases in animal production, thus implementation of cooling technologies is a key factor for alleviating these negative consequences. In pig facilities, various cooling methods have been implemented. Air temperature may be decreased by using adiabatic cooling technology such as a high-pressure fogging system or evaporative pads. In modern-type buildings large-surface evaporative pads may support a tunnel ventilation system. Currently a lot of attention has also been paid to developing energy- and water-saving cooling methods, using for example an earth-air or earth-to-water heat exchanger. The pigs' skin surface may be cooled by using sprinkling nozzles, high-velocity air stream or conductive cooling pads. The effectiveness of these technologies is discussed in this article, taking into consideration the indicators of animal welfare such as respiratory rate, skin surface and body core temperature, performance parameters and behavioural changes.

Perspective review of optical imaging in welfare assessment in animal-based research

To refine animal research, vital signs, activity, stress, and pain must be monitored. In chronic studies, some measures can be assessed using telemetry sensors. Although this methodology provides high-precision data, an initial surgery for device implantation is necessary, potentially leading to stress, wound infections, and restriction of motion. Recently, camera systems have been adapted for animal research. We give an overview of parameters that can be assessed using imaging in the visible, near-infrared, and thermal spectrum of light. It focuses on heart activity, respiration, oxygen saturation, and motion, as well as on wound analysis. For each parameter, we offer recommendations on the minimum technical requirements of appropriate systems, regions of interest, and light conditions, among others. In general, these systems demonstrate great performance. For heart and respiratory rate, the error was <4  beats  /  min and 5 breaths/min. Furthermore, the systems are capable of tracking animals during different behavioral tasks. Finally, studies indicate that inhomogeneous temperature distribution around wounds might be an indicator of (pending) infections. In sum, camera-based techniques have several applications in animal research. As vital parameters are currently only assessed in sedated animals, the next step should be the integration of these modalities in home-cage monitoring.