System and method for continuous physiological monitoring of livestock

Abstract

A monitoring device for capturing data about a subject is disclosed. The monitoring device includes a wireless power transfer receiver, a micro-electromechanical system (MEMS) sensor, and a transmitter. The wireless power transfer receiver is configured to convert wireless electromagnetic radiation into electrical energy supplied to a conductor. The MEMS sensor is operable to receive the electrical energy and generate an electrical signal based on a mechanical wave associated with the subject. The transmitter is configured to transmit a wireless signal based on the electrical signal generated by the MEMS sensor.

Description

SYSTEM AND METHOD FOR CONTINUOUS PHYSIOLOGICAL MONITORING OF LIVESTOCK CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The current patent application is a non-provisional utility patent application which claims priority benefit of earlier-filed U.S. Provisional Application Ser. No. 63 / 740,776; titled “SYSTEM AND METHOD FOR CONTINUOUS PHYSIOLOGICAL MONITORING OF LIVESTOCK”; and filed December 31, 2024. The Provisional Application is hereby incorporated by reference, in its entirety, into the current patent application.BACKGROUND OF THE INVENTION

[0002] Bovine Respiratory Disease Complex (BRDC) remains the largest health issue affecting cattle and is responsible for 75% of feedlot morbidity and 50-70% of feedlot deaths. Current detection methods rely primarily on visual inspection, which often results in delayed diagnosis and treatment. Early detection and treatment are crucial to prevent severe outcomes and reduce disease spread. Additionally, delayed antibiotic administration can contribute to antimicrobial resistance (AMR), a growing global health challenge.

[0003] Thus, there is a need for an improved for BRDC detection techniques. This background discussion is intended to provide information related to the present invention which is not necessarily prior art.SUMMARY OF THE INVENTION

[0004] Embodiments of the current invention address one or more of the above-mentioned problems and provide a distinct advance in the art of monitoring subjects, such as bovines or other animals.

[0005] One embodiment of the invention is a monitoring device for capturing data about a subject. The monitoring device includes a wireless power transfer receiver, a microelectromechanical system (MEMS) sensor, and a transmitter. The wireless power transfer receiver is configured to convert wireless electromagnetic radiation into electrical energy supplied to a conductor. The MEMS sensor is operable to receive the electrical energy and generate an electrical signal based on a mechanical wave associated with the subject. The transmitter is configured to transmit a wireless signal based on the electrical signal generated by the MEMS sensor.

[0006] Another embodiment of the invention is a system for monitoring a subject. The system includes a monitoring device described above, a wireless power transfer transmitter, and a receiver. The wireless power transfer transmitter is configured to transmit the wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device. The receiver is configured to receive the wireless signal from the transmitter of the monitoring device.

[0007] Another embodiment of the invention is a method of capturing data of a subject. The method includes securing a monitoring device as described above to the subject; transmitting, via a wireless power transfer transmitter, wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device; and receiving, via a receiver, a wireless signal from the transmitter of the monitoring device.

[0008] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Other aspects and advantages of the current invention will be apparent from the following detailed description of the embodiments and the accompanying drawing figures.BRIEF DESCRIPTION OF DRAWINGS

[0009] Embodiments of the current invention are described in detail below with reference to the attached drawing figures, wherein:

[0010] FIG. l is a block diagram depicting selected components of a system constructed according to embodiments of the present invention;

[0011] FIG. 2 is a block diagram depicting selected components of a monitoring device of the system of FIG. 1;

[0012] FIG. 3 is a block diagram depicting selected components of sensor of the monitoring device of FIG. 2;

[0013] FIG. 4 is a block diagram depicting selected components of the system of FIG. 1 including mobile data receivers and wireless power transfer transmitters mounted on mobile platforms;

[0014] FIG. 5 is a data architecture implemented in the system of FIG. 1 according to embodiments of the invention; and

[0015] FIG. 6 is a flowchart depicting exemplary steps of a method according to an embodiment of the present invention.

[0016] The drawing figures do not limit the current invention to the specific embodiments disclosed and described herein. The drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the invention.DETAILED DESCRIPTION OF THE INVENTION

[0017] The following detailed description of the technology references the accompanying drawings that illustrate specific embodiments in which the technology can be practiced. The embodiments are intended to describe aspects of the technology in sufficient detail to enable those skilled in the art to practice the technology. Other embodiments can be utilized and changes can be made without departing from the scope of the current invention. The following detailed description is, therefore, not to be taken in a limiting sense. The scope of the current invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0018] Embodiments of the present invention provide comprehensive systems for monitoring livestock herd (and / or the health of other animal(s)) through an array of innovative sensors and data collection methods. Embodiments include battery -free wearable devices that can be integrated into existing ear tags or other wearable forms, powered through strategic placement of radio-frequency (RF) antenna arrays at, for example, feeding and drinking locations. Embodiments enable continuous monitoring of vital signs and early detection of health issues, particularly respiratory diseases.

[0019] A livestock monitoring system according to one embodiment includes a battery-free ‘tag’ attachable to livestock and a network of RF power transmitters. The tag incorporates a micro-electromechanical-system (MEMS)-based vibration sensor that detects cardiopulmonary signals and a radio-frequency-to-direct current (RF-to-DC) converter that harvests energy from strategically positioned RF power transmitters. The RF power transmitters are positioned at feed bunks, water stations, and other livestock gathering areas to ensure consistent power delivery. The tag (e.g., sensor) samples and analyzes cardiopulmonary and movement data to determine vital signs including heart rate, respiratory rate, and heart rate variability. Machine learning algorithms may be employed by a processing element on the tags and / or at base stations nearby to processthis data to detect early signs of illness, particularly respiratory diseases. However, other methods may be employed for processing data, including signal processing and statistical modeling. The system enables continuous health monitoring without battery replacement while minimizing human intervention. Power management techniques including adaptive duty cycling and synchronized wake-up scheduling optimize the tag's operation based on available RF energy and monitoring requirements.

[0020] Turning to FIG. 1, a system 10 constructed according to an embodiment of the invention for monitoring one or more subjects 12 is schematically depicted. The subjects 12 are depicted as bovines, but may be any other animal subject without departing from the scope of the present invention, including an equine, ruminant, camelid, endangered species, etc. The system 10 is operable to collect data related to the subjects 12, including data indicative of a heart rate, a heart rate variability, a murmur, an arrythmia, a lung sound, a cough, a burp, a movement of the subject, a reproduction mounting attempt related to the subject, an altered gait, a fetal heart rate, a fetal movement, a rumen contraction, a regurgitation, a parturition, a cecal contraction, or the like, as described in further detail below. In one or more embodiments, the system 10 includes one or more monitoring devices 14, 16, 18, one or more wireless power transfer transmitters 20, 22, one or more data receivers 24, 26, one or more local servers 28, and one or more remote devices 30.

[0021] The monitoring devices 14, 16, 18 may be part of an ear tag 14, a collar 16, or an adhesive patch 18. However, the monitoring devices 14, 16, 18 may take any form without departing from the scope of the present invention, including a tail base attachment, an intracervical insertion, an implantable device, a swallowable device, or any other wearable device, or the like. Turning to FIG. 2, a representative monitoring device 14 is depicted. The other monitoring devices 16, 18 may comprise substantially similar components. The monitoring devices 14 are configured to receive RF energy when in range to the wireless power transfer transmitters 20, 22, and use that energy to capture data associated with their respective subjects 12 and transmit that data to the data receivers 24, 26. In one or more embodiments, the monitoring device 14 comprises a housing 32, a wireless power transfer receiver 34, one or more sensors 36, a transmitter 38, and a controller 40.

[0022] The housing 32 that encloses at least a portion of the wireless power transfer receiver 34, the sensor(s) 36, the transmitter 38, and / or the controller 40. The housing 32 may form any number of shapes or packages depending on how the device 14 is to be secured to thesubject 12. As discussed previously, the housing 32 may form at least a part of or be operable to be attached to an ear tag, collar, body patch, intracervical device for parturition monitoring, tail base for mounting monitoring, etc., providing flexibility in deployment and ensuring that the system can adapt to different environments and husbandry protocols. In some embodiments, the housing 32 mechanically engages an existing ear-button post to improve retention and vibrational coupling to the pinna, thereby increasing signal-to-noise ratio for cardiopulmonary and acoustic sensing. In one or more embodiments, the monitoring devices 14, 16, 18 may include a visual indicator 33 that may be visible outside of the housing 32 for indicating a detected trend or metric to alert users when the subject 12 is near the receivers 24, 26 or otherwise proximal to a certain location 56 (e.g., feed bunk, chute, watering trough, etc.). The indicator 33 may comprise a light, such as an LED or the like, or an e-ink badge operable to persist without power. The controller 40 may be configured to activate the indicator 33 when the controller 40 detects a cardiopulmonary trend, such as detection of a heart rate or respiratory rate outside of a predefined range.

[0023] The wireless power transfer receiver 34 configured to convert wireless electromagnetic radiation into electrical energy for supplying power to the other components of the device 14. In one or more embodiments, the wireless power transfer receiver 34 comprises an antenna 42 and an RF-to-DC converter 44. The wireless power transfer receiver 34 may be a dualband RF power harvesting system configured to harvest RF energy in the 900 MHz band (about 902 MHz to about 928 MHz) and harvest RF energy in the 2.4 GHz band (about 2.4 GHz to about 2.485 GHz). The wireless power transfer receiver 34 may have a power conversion efficiency greater than 40% and in one or more embodiments, greater than 60%. The wireless power transfer receiver 34 may have an operating voltage range of about 2 V to about 3.6 V. In one or more embodiments, one or more power sources 45 may be connected to the wireless power transfer receiver 34 for providing electrical power to the components of the monitor 14. The power source 45 may comprise a rechargeable battery, capacitor, or the like.

[0024] In one or more embodiments, the sensor(s) 36 is operable to receive at least a portion of the electrical energy from the wireless power transfer receiver 34 and generate one or more electrical signals based at least in part on one or more metrics of the subject 12. In one or more embodiments, the sensor(s) 36 may be connected to an analog-to-digital converter 46, which receives analog signals from one or more of the sensors 36, converts the analog signals into digitalsignals, and sends the digital signals to the controller 40. The analog-to-digital converter 46 may be a 16-bit ADC and be configured to sample signals at up to 20kHz.

[0025] Turning to FIG. 3, the sensor(s) 36 may include an acoustic accelerometer 48, an inertial measurement unit (IMU) 50, electrocardiogram (EKG) probes 52, and / or a pulse oximeter sensor or photoplethysmography sensor 54, which may operate independently or in concert to provide comprehensive physiological monitoring. The acoustic accelerometer 48 may be configured to generate electrical signals based on one or more mechanical waves associated with the subject 12. The acoustic accelerometer 48 may function as an acoustic microphone for capturing rumen and digestive sounds, providing additional data on the digestive health of the subject 12. The microphone 48 enables the system 10 to detect and characterize abnormal rumen motility patterns or signs of digestive disorders such as bloat, acidosis, or displaced abomasum, which are critical in the early identification of health concerns in feedlot operations. The system may analyze the frequency, duration, and intensity of rumen contractions to establish baseline digestive patterns and identify deviations that may indicate developing health issues.

[0026] In one or more embodiments, the acoustic accelerometer 48 includes a MEMS sensor. The MEMS sensor may comprise any of the MEMS devices (such as a resonator, gyroscope, accelerometer, etc.) described and / or depicted in U.S. Patent No. 11,137,250 and / or U.S. Patent No. 11,678,113, which are hereby incorporated by reference herein. The acoustic accelerometer 48 may be configured to detect frequencies, and / or have a frequency response, in range from about 1 kHz to about 9 kHz, from about 500 Hz to about 9.5 kHz, about 0 Hz to about 10 kHz, or about 0 Hz to about 50 kHz. This broad frequency response range enables the system 10 to distinguish between various physiological events including eructation, respiratory sounds, cardiac sounds, and digestive processes, and quantify their respective duration, intensity, and temporal patterns. The system may employ signal processing algorithms to isolate and characterize specific acoustic signatures associated with different physiological events. In one or more embodiments, the acoustic accelerometer 48 has a sensitivity of at least 50 fF / ps, at least 60 fF / ps, at least 73 fF / ps, or at least 100 fF / ps. In one or more embodiments, the acoustic accelerometer 48 has a noise floor less than 5mg Hz, less than 2.5mg Hz, less than Img / ^Hz, or less than 0.5mg / ^Hz. In one or more embodiments, the acoustic accelerometer 48 may comprise a dualpurpose MEMS sensor array capable of simultaneous monitoring of multiple physiological parameters.

[0027] In one or more embodiments, the controller 40 is configured to operate the acoustic accelerometer 48 in an acoustic mode to detect vocalizations. In one or more embodiments, the acoustic accelerometer may have a sample rate of at least 2 kHz. For example, the controller 40 is configured to operate the acoustic accelerometer 48 to detect 100-8,000 Hz sounds representative of bovine vocalizations, 100-400 Hz representative of cough energy. In one or more embodiments, the controller 40 is configured to implement feature extraction, such as determining a zero-crossing rate, a spectral centroid, harmonicity, or the like. The controller 40 may also be configured to determine a vocal event counter per interval and detect a distress call based on one or more reference waveforms of a representative distress call. The controller 40 may be configured to execute band-pass filtering (e.g., 100-3,000 Hz) and extract features comprising zero-crossing rate, spectral centroid, spectral flux, and harmonicity. The controller 40 may be configured to time-stamp detected events and count the events per interval for correlation with clinical outcomes.

[0028] In one or more embodiments, the controller 40 is configured to transition operation of the acoustic accelerometer 48 between a relatively low-frequency sampling mode and a burstacquisition mode. The low-frequency sampling mode may be the default mode in which the controller 40 samples the output signals of the acoustic accelerometer 48 at a default rate, such as one sample per hour. In the burst-acquisition mode, the controller 40 may sample the output signals of the acoustic accelerometer 48 at a burst rate that is greater than the default rate for a predetermined duration. For example, the burst may be at least around 100 Hz, and the duration may be at least around thirty seconds.

[0029] In one or more embodiments, the IMU 50 is configured to capture comprehensive movement data of the subject 12. The IMU 50 may include six-axis accelerometers to track the subject's movement, posture, and gait in three-dimensional space. These sensors provide continuous data on locomotion patterns, activity levels, and postural changes, allowing the system 10 to detect subtle variations that may indicate lameness, abnormal movement patterns, or early signs of injury. In one or more embodiments, the IMU 50 is used to detect the heart rate of the subject 12 with the IMU 50 axis at 25-50 Hz with band-pass filtering (e.g., 5-25 Hz). The IMU 50 is used to detect the respiratory rate via band-pass filtering (e.g., 0.1-1 Hz envelope), vocalization via band-pass filtering (e.g., 2-10 kHz with 20-100 millisecond windows), and cough detection by determining short-time energy of the detected signal with spectral flatness. Thesystem may establish baseline movement patterns for each subject and detect deviations that could indicate developing health issues. This adds another layer of health monitoring, particularly relevant for issues such as hoof injuries, musculoskeletal disorders, or neurological conditions affecting mobility. The IMU 50 may likewise include one or more MEMS sensors configured to operate in coordination with other sensor systems to provide integrated movement and physiological data.

[0030] In one or more embodiments, the EKG probes 52 are configured to capture detailed EKG data through multiple lead configurations. In one or more embodiments, two EKG probes 52 are positioned on one or more the monitoring devices 14, 16, 18 to form a circuit across the heart of the subject 12. For example, an ear tag 16 having an EKG probe 52 may be placed on each ear of the subject 12. The EKG probes 52 provide precise measurements of cardiac electrical activity and heart function, enabling detection of subtle rhythm abnormalities and conduction disturbances. The system 10 may use the EKG data to complement the MEMS sensor's 48, 50 vibration-based data, offering a more comprehensive view of the heart's electrical and mechanical activity. This multi-modal approach to cardiac monitoring enables advanced diagnostics including detection of arrhythmias, conduction abnormalities, acute or slowly progressive changes in heart rate and rhythm associated with stress, onset of cardiopulmonary disease, nutritional deficits, identification of fetal heart activity, and other cardiac conditions that may not be apparent through singlemodality monitoring. The integration of EKG and vibration data provides a more complete assessment of cardiovascular health and enables early detection of developing cardiac conditions.

[0031] In one or more embodiments, the pulse oximeter 54 is configured to capture continuous oxygen saturation data and plethysmographic waveforms. The pulse oximeter 54 may be embedded into the system to measure oxygen saturation in the blood through multiple wavelength optical sensing. This capability is critical in monitoring respiratory function and detecting early signs of respiratory distress, which can indicate the onset of Bovine Respiratory Disease (BRD) and other lung-related issues. The system analyzes both the absolute oxygen saturation values and their temporal variations, providing insight into both acute and chronic respiratory conditions. Combining pulse oximetry data with heart rate, heart rate variability, and respiratory measurements allows for more effective monitoring of overall cardiopulmonary function and enables early detection of developing respiratory conditions.

[0032] Turning back to FIG. 2, the transmitter 38 is configured to transmit one or more wireless signals based at least in part on the one or more electrical signals generated by the sensor(s) 36. The transmitter 38 may alternatively be a transceiver antenna operable to both transmit and receive wireless signals. For example, in one or more embodiments, the transmitter 38 is a radio-frequency identification (RFID) tag. In one or more embodiments, the transmitter 38 comprises a transceiver configured to transmit ultra-high frequency signals using a low-energy protocol, such as a Bluetooth® low energy (BLE) transceiver, implementing advertising and / or generic attribute profiles (GATT). Further, the transmitter 38 may be configured to broadcast advertising packets containing device identifiers and summary features, and to establish GATT connections with nearby receivers 24, 26, or gateways, for bulk data offload, time synchronization, firmware updates and other over the air updates. The gateways may forward data via wireless communication protocols, including LoRaWAN, Wi-Fi, LTE, or the like. In one or more embodiments, controller 40 is configured to implement, via the transmitter 38, feature-first uploads (per-minute aggregates) with raw waveform snippets only for flagged intervals with advertising every 2-5 seconds when the controller 40 determines the monitoring device is within range of the receivers 24, 26 with GATT uploads of 5 seconds or less. The transmitter 38 may comprise any number or type of wireless communication elements without departing from the scope of the present invention. For example, the transmitter 38 may comprise a satellite communication element, a cellular communication element, or the like.

[0033] In one or more embodiments, the controller 40 comprises one or more processing elements in communication with the sensor(s) 36 (by way of the ADC 46) and configured to sample the one or more electrical signals generated by the sensor(s) 36 to produce sample data. In one or more embodiments, the controller 40 further includes one or more memory elements, such as flash memory, for storing various data, the sample data, and / or an identifier for the subject 12. The one or more memory elements may a comprise an 8 to 64 MB NOR flash unit that implements circular buffering and cyclic redundancy checks. The controller 40 may be configured to have the sample data transmitted via the transmitter 38 when in vicinity of the RF base stations 20, 22. Additionally, in one or more embodiments, the controller 40 is configured to implement adaptive duty cycling based on at least one of an available radio-frequency energy, a sensor sampling requirement, or a transmitter communication requirement. In one or more embodiments, the controller 40 is configured to implement activity -gated duty cycling. During low activity or rest,the controller 40 may be configured to sample infrequently (e.g., once every 15-60 min) in order to, for example, log baseline resting heart / respiratory metrics. Upon detecting elevated motion via the on-board accelerometer and / or IMU, the controller 40 may be configured to switch into a high-power, high-rate acquisition mode (e.g., 100-800 Hz for the IMU and optionally 2-10 kHz for acoustic accelerometry) for a fixed window (e.g., 30-120 seconds) to capture clean cardiopulmonary waveforms and selected behavioral signatures (cough, tachypnea, rumen events). This preserves energy and increases RF-energy neutrality by concentrating heavier compute / transmit when motion suggests informative physiology (exercise, distress, BRD symptoms, or the like). In one or more embodiments, the controller 40 implements a two-level duty-cycle policy comprising low-frequency sampling at <1 sample per hour during low-activity states and burst sampling at >100 Hz for >30 seconds upon accelerometer-derived activity threshold crossing. Such a hysteresis window avoids rapid state flapping. In one or more embodiments, the controller 40 is configured to adapt sampling and transmission intervals based at least on available harvested RF energy, battery state (if present), and detected activity, thereby maintaining operation under constrained energy budgets.

[0034] Turning back to FIG. 1, the wireless power transfer transmitters 20, 22 are configured to transmit the wireless electromagnetic radiation to the wireless power transfer receivers of the monitoring devices 14, 16, 18. In one or more embodiments, the wireless power transfer transmitters 20, 22 are configured to transmit RF energy in the 900 MHz band at up to +30 dBm. In one or more embodiments, the wireless power transfer transmitters 20, 22 are configured to additionally or alternatively transmit RF energy in the 2.4 GHz band at up to +20 dBm. The wireless power transfer transmitters 20, 22 are configured to transmit RF energy with a 3dB beam width of ±35° at one or both frequencies.

[0035] The data receivers 24, 26 are configured to receive the one or more wireless signals from the transmitters of the monitoring devices 14, 16, 18. The data receivers 24, 26 are configured to receive data transmissions from the monitoring devices 14, 16, 18. The data receivers 24, 26 may also be in wired and / or wireless communication with the local server 28 and / or the remote device 30. The data receivers 24, 26 may be configured to relay the data to the local server 28 and / or the remote device 30, including any alerts generated by the controller of one or more of the monitoring devices 14, 16, 18. In one or more embodiments, the data receivers 24, 26 areconfigured to buffer data received from the monitoring devices 14, 16, 18 and transmit the data via a low-power wide-area backhaul.

[0036] The wireless power transfer transmitters 20, 22 and data receivers 24, 26 may be positioned at locations 56 where the subjects 12 frequently gather. For example, the wireless power transfer transmitters 20, 22 and data receivers 24, 26 may be located on a feed bunk 56, a water station, a gathering area, a processing area, a fence, a transport vehicle / trailer, or the like.

[0037] In one or more embodiments, one or more of the wireless power transfer transmitters 20, 22 and data receivers 24, 26 are mobile and positioned on one or more mobile vehicles. Turning to FIG. 4, mobile wireless power transfer transmitters 20, 22 and data receivers 24, 26 may be employed. The wireless power transfer transmitters 20, 22 and data receivers 24, 26 may be mounted on one or more mobile vehicles 58, 60, such as an aerial drone 58 and / or a land-based vehicle 60. The mobile vehicles 58, 60 may be configured to autonomously patrol one or more areas proximal to the subjects 12 so that the mobile wireless power transfer transmitters 20, 22 and data receivers 24, 26 can obtain the data from the transmitters of the monitoring devices 14 when the subjects are outside of the range of the stationary data bridges (for example, when the subjects are over 10 meters from the stationary data bridges). The mobile vehicles 58, 60 may be equipped with long-range RFID antennas to automate the process of locating and identifying subjects 12 in distress and may include position-detection devices. In one or more embodiments, the aerial drone 58 is configured to fly over the feedlot, scan for signals from the monitoring devices 14, and pinpoint the location of sick or distressed cattle. This saves time and labor, reducing the burden on human workers while enabling faster response times to emerging health concerns. The mobile vehicles 58, 60 may be programmed to autonomously patrol the feedlot at regular intervals, providing continuous surveillance of subject health.

[0038] In one or more embodiments, one or more of the mobile wireless power transfer transmitters and data receivers may be mounted on a handheld long-range reader 62. The handheld reader 62 includes antennas and circuitry for long-range capabilities that allow users to easily identify and locate specific subjects that are experiencing medical issues. The readers 62 interface with the sensors worn by the subjects 12, enabling users to scan the area for subjects 12 that need to be pulled from the herd for closer examination or treatment. This enhances workflow efficiency, especially in large feedlots where subjects, such as cattle, may be difficult to locate.

[0039] The local server 28 is in communication with the data receivers 24, 26 and is configured to receive the data associated with the subjects 12. The local server 28 may comprise one or more communication elements, one or more memory elements, one or more positiondetection devices, a user interface, and one or more processing elements.

[0040] The local server 28 may have stored on its memory element instructions that implement a multi-layered analysis framework for processing physiological data. When receiving raw data from the data receivers 24, 26, the system 10 employs sophisticated signal processing algorithms to sample the sensor(s), including but not limited to the MEMS-based vibration sensor, to obtain cardiopulmonary data. The system performs real-time analysis of the cardiopulmonary data through multiple processing stages to determine vital signs of the livestock animal. The analysis pipeline incorporates adaptive filtering techniques to minimize environmental noise and motion artifacts while preserving physiologically relevant signals. The system employs machine learning models trained on extensive livestock health data to detect anomalies in the vital signs indicative of potential illness. These models incorporate temporal pattern recognition to identify subtle deviations from individual baseline measurements that may indicate developing health issues. The system can determine at least one of a heart rate, a heart rate variability, a murmur, an arrhythmia, a lung sound, a cough, a burp, a movement of the subject, a reproduction mounting attempt related to the subject, an altered gait, a fetal heart rate, a fetal movement, a rumen contraction, a regurgitation, a parturition, or a cecal contraction. The local server 28 executes specialized machine learning algorithms optimized for early detection of respiratory diseases, incorporating both supervised and unsupervised learning approaches for pattern recognition. The system implements hierarchical models for disease probability estimation, illness severity assessment, and treatment response tracking, enabling comprehensive health monitoring and management.

[0041] The local server 28 implements advanced cardiovascular monitoring algorithms to perform temporal tracking of heart rate dynamics, heart rate variability metrics, and cardiac rhythm abnormalities. The system employs sophisticated signal processing techniques to identify and characterize cardiac murmurs and arrhythmias, analyzing both their temporal and spectral characteristics. For respiratory monitoring, the system tracks not only basic respiratory rate but also implements advanced acoustic analysis to characterize breath sounds and identify abnormal patterns such as crackles, rales, or wheezes. The system employs specialized algorithms fordetecting and characterizing cough events, analyzing their frequency, intensity, and temporal patterns to assess respiratory health. Environmental impact monitoring is achieved through sophisticated analysis of eructation events (burps), employing machine learning models to estimate methane emission volumes and patterns. For example, when a vibrational pattern indicating a regurgitation event is detected, this may be used by the system to add to a regurgitation event tally or record. The system may be configured to determine an estimated emissions amount based on the total number of regurgitation events recorded. The system aggregates this data across the herd to generate environmental pollution calculations and trending analyses, providing valuable insights for environmental management and sustainability initiatives.

[0042] The local server 28 implements sophisticated movement tracking and analysis capabilities. Within monitored areas, the system employs advanced positioning algorithms to track subject movement patterns, social interactions, and behavioral changes that might indicate health issues. For subjects in remote areas such as pastures, the system utilizes adaptive power management and intermittent sampling strategies to maintain monitoring capabilities while optimizing power consumption. The movement analysis algorithms incorporate contextual information about environmental conditions and time of day to establish normal behavioral patterns and identify meaningful deviations.

[0043] The system implements specialized algorithms for reproductive health monitoring. For male subjects, the system employs advanced motion analysis to detect and characterize mounting attempts, analyzing the vibrational signatures and movement patterns to assess breeding behavior and efficiency. The resulting statistics provide valuable insights into reproductive performance and herd management. For pregnancy monitoring, the system employs sophisticated signal processing to detect and track fetal heart rates and movements, enabling early pregnancy confirmation and ongoing monitoring of fetal health. The system can detect parturition events through analysis of behavioral and physiological changes, providing timely alerts for animal care staff. For enhanced monitoring during critical periods, the system supports alternative sensor placements, including intravaginal or cervical positions, with specialized algorithms optimized for these configurations.

[0044] The local server 28 implements advanced biomechanical analysis algorithms for lameness detection. The system analyzes complex movement patterns using sophisticated signal processing techniques to identify subtle changes in gait characteristics, weight distribution, andmovement symmetry. These algorithms incorporate machine learning models trained on extensive datasets of normal and abnormal gait patterns, enabling early detection of developing lameness conditions before they become visually apparent.

[0045] The system employs advanced acoustic and vibrational analysis algorithms for monitoring gastrointestinal function. These algorithms can identify and characterize specific digestive events including rumen contractions, eructation events, and cud regurgitation. The system analyzes the frequency, intensity, and temporal patterns of these events to establish normal digestive function baselines and identify deviations that may indicate developing health issues. This analysis is particularly valuable for early detection of common digestive disorders in livestock.

[0046] The comprehensive integration of these monitoring capabilities enables unprecedented real-time health tracking. The system continuously analyzes multiple physiological parameters including heart rate, respiratory rate, oxygen saturation, digestive function, and movement patterns, creating a holistic view of animal health status. Advanced data fusion algorithms combine inputs from multiple sensors to generate more reliable and comprehensive health assessments. The system employs sophisticated trend analysis to detect subtle changes that may indicate developing health issues, enabling proactive intervention before conditions become severe. This predictive capability significantly improves treatment outcomes while reducing overall healthcare costs. The system can adapt its monitoring parameters based on individual animal characteristics and environmental conditions, ensuring optimal sensitivity and specificity in health assessment.

[0047] The local server 28 may be configured to perform predictive health analytics. By combining the data collected from the various sensors, the local server 28 may employ machine learning algorithms to predict the likelihood of certain diseases or conditions, such as BRD, lameness, reproductive health issues, and / or digestive disorders. These predictive analytics allow operators to proactively manage herd health, reducing morbidity and mortality rates while improving overall herd productivity and efficiency. This system 10 also provides a means to improve animal welfare overall by facilitating earlier intervention for individual and herd health issues.

[0048] The local server 28 also enables integrated reporting with feedlot management systems. All health data collected by the monitoring devices, including alerts generated by thehandheld or drone readers, can be automatically uploaded to herd management platforms on one or more remote devices. This allows for seamless integration of health monitoring data into the broader management workflow, ensuring that health interventions are coordinated with other operational activities, such as feeding, vaccination, and movement of cattle between pens.

[0049] The system 10 may also be implemented on mobile devices, such as trailers. The is especially advantageous when shipping ruminants from low atmospheric areas to higher altitude areas - these cattle are at substantial risk of ‘brisket disease’. Data captured by the monitoring devices that is flagged by the local server 28 enable user to identify those cattle developing clinical signs sooner, thereby preventing certain morbidity and reducing treatment cost.

[0050] In one or more embodiments, the local server 28 may additionally or alternatively be configured to determine an amount of methane released by the subjects 12 based at least in part on the data. Turning to FIG. 5, an architecture for measuring, reporting, and verifying methane data implemented in embodiments of the invention is depicted. The architecture may be implemented in the system 10. The architecture may include a sensor layer in which the MEMS accelerometer sensor array is configured for acoustic detection.

[0051] The data from the sensor may then undergo signal processing via the controller of the monitoring device and / or the local server. The signal processing may include receiving the raw vibration signal with characteristic eructation signature, filtering and processing the signal with event detection, and then classifying and quantifying the detected events.

[0052] The data associated with the detected events may then be relayed to a user interface of the local server and / or to a remote device. For example, the data may be implemented in a graphical representation of, for example, a detected burp event waveform. The data may include temporal characteristics (duration), frequency components, and / or amplitude signatures.

[0053] The architecture may further include a data aggregation layer. The local server may receive multiple tag data streams, perform local signal validation processes, and perform data aggregation processes. A user interface at the local server and / or at a remote device may display a real-time monitoring dashboard that includes individual animal emission events, herd-level statistics, and any detected trends.

[0054] The architecture may further include a verification layer that includes a processing function and a reporting function. The processing function may include estimating emission calculations, carbon credit quantification, and / or feed additive efficacy analysis. The reportingfunction may include daily emission estimate summaries, carbon credit documentation, and / or feed optimization recommendations.

[0055] FIG. 5 demonstrates the flow of data from individual sensor detection through aggregation and processing to final verification and reporting, with arrows indicating data movement between layers. Key calibration points and validation steps are highlighted to illustrate the robustness of the MRV process.

[0056] Embodiments of the invention enable accurate measurement, reporting, and verification of methane emissions. The system 10 is operable to detect and quantify methane emissions from cattle through both burping (enteric methane) and flatulence. The quantification of the methane may be accomplished through tracking a number of methane emission events, the respiration rate of the subject, an estimated amount of methane emitted based on the emission events and / or the respiration rate, and / or one or more additional auxiliary sensors. This enables feedlot operators and sustainability -focused organizations to track real-time methane outputs from individual cattle. This not only helps optimize feeding strategies but also ensures that the impact of methane-reducing feed additives can be accurately measured and reported.

[0057] Additionally, by quantifying methane emission reductions, the system 10 can generate critical data that aligns with carbon credit markets, allowing dairy and beef farmers to monetize their methane reduction efforts. This offers users a new revenue stream while supporting broader ESG goals.

[0058] The flow chart of FIG. 6 depicts the steps of an exemplary method 600 of capturing data of a subject, such as livestock or the like. In some alternative implementations, the functions noted in the various blocks may occur out of the order depicted in FIG. 6. For example, two blocks shown in succession in FIG. 6 may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order depending upon the functionality involved. In addition, some steps may be optional.

[0059] The method 600 is described below, for ease of reference, as being executed by exemplary devices and components introduced with the embodiments illustrated in FIGS. 1-5. The steps of the method 600 may be performed by the control system through the utilization of processors, transceivers, hardware, software, firmware, or combinations thereof. However, some of such actions may be distributed differently among such devices or other devices without departing from the spirit of the present invention. Control of the system may also be partiallyimplemented with computer programs stored on one or more non-transient computer-readable medium(s). The computer-readable medium(s) may include one or more executable programs stored thereon, wherein the program(s) instruct one or more processing elements to perform all or certain of the steps outlined herein. The program(s) stored on the computer-readable medium(s) may instruct processing element(s) to perform additional, fewer, or alternative actions, including those discussed elsewhere herein.

[0060] Referring to step 602, the monitoring device(s) are secured to the subject(s). As discussed above, the monitoring devices may be secured as an ear tag, collar, adhesive patch, or the like.

[0061] Referring to step 604, the controller implements adaptive duty cycling of one or more of the sensors with a relatively lower default sample rate. The controller receives the data from the sensors and processes it for detection of certain activity.

[0062] Referring to step 606, the controller switches to a burst acquisition mode for one or more of the sensors. The controller then receives data from one or more of the sensors at a higher sample rate and stores the data for transmission.

[0063] Referring to step 608, wireless electromagnetic radiation is transmitted, via a wireless power transfer transmitter, to the wireless power transfer receiver of the monitoring device. This step may include continuously transmitting the radiation and / or transmitting when the subjects are detected nearby. This step may include transporting, via the mobile vehicle, the wireless power transfer transmitter in proximity to the subjects. This step may include harvesting RF energy from strategically positioned RF power transmitters to power the monitoring devices secured to the subjects. This step may further include detecting data associated with the subjects via one or more sensors on the monitoring devices. For example, the data may be representative of cardiopulmonary signals from a livestock animal using a MEMS-based vibration sensor in the monitoring device.

[0064] Referring to step 610, one or more wireless signals are received, via one or more of the data receivers, from the transmitters of the monitoring devices. This step may include transporting, via the mobile vehicle, the device reader in proximity to the subjects. For example, this step may include autonomously patrolling, via the mobile vehicle, an area proximal to the subjects so that the mobile reading devices can obtain the one or more wireless signals from the transmitters of the monitoring devices. In one or more embodiments, this step includestransmitting, via the monitoring device transmitter, ultra-high frequency signals using a low-energy protocol. Once the monitoring device transmitter connects with the data receiver, the monitoring device transmitter uploads data stored on the monitoring device.

[0065] Referring to step 612, the data associated with the subjects is received by the local server. This step may include receiving at the local server the data from the data receivers through wired and / or wireless communication.

[0066] Referring to step 614, the data associated with the subject is analyzed by the local server. As discussed above, the data may be associated with a heart rate, standard heart rate variability metrics (e.g., total power, low frequency power, high frequency power, standard deviation of R-R intervals [SDNN], etc.), a respiratory rate, a presence of coughing events, a respiratory rate variability, intrapulmonary rales or crackles, gastrointestinal signals from a livestock animal using a MEMS-based vibration sensor in the tag, etc.

[0067] This step may include detecting and analyzing gastrointestinal signals to determine rumination behavior (i.e., chewing of ‘cud’), burps produced, a presence of displaced abomasum, a presence of rumen bloat / tympany, an abnormal ruminal contraction pattern, or the like.

[0068] This step may include detecting reproductive health signals from the subjects using data collected by one of the MEMS-based vibration sensors of the monitoring devices. This may include analyzing the reproductive health signals to determine bull-heifer or bull-cow breeding events, bull mounting / breeding rate, bull lameness or problems with mounting cows / heifers, early (< 90 days) presence of in-utero fetus (calf) development / movement, presence of fetal death / abortion events when fetal signals cease, parturition events (giving birth), abnormal parturition (i.e., dystocia) events, or the like. This step may include comparing the determined vital signs, physiologic events, and reproductive signals against baseline values to detect anomalies.

[0069] Referring to step 616, one or more wireless signals representative of the data is transmitted to a remote device. This may include transmitting one or more wireless signals representative of an alert when anomalies indicating potential illness, injury, or reproductive concern are detected.

[0070] The method 600 may include additional, less, or alternate steps and / or device(s), including those discussed elsewhere herein.

[0071] Throughout this specification, references to “one embodiment”, “an embodiment”, or “embodiments” mean that the feature or features being referred to are included in at least oneembodiment of the technology. Separate references to “one embodiment”, “an embodiment”, or “embodiments” in this description do not necessarily refer to the same embodiment and are also not mutually exclusive unless so stated and / or except as will be readily apparent to those skilled in the art from the description. For example, a feature, structure, act, etc. described in one embodiment may also be included in other embodiments, but is not necessarily included. Thus, the current invention can include a variety of combinations and / or integrations of the embodiments described herein.

[0072] Although the present application sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this patent and equivalents. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical. Numerous alternative embodiments may be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.

[0073] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0074] Certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software(eg., an application or application portion) as computer hardware that operates to perform certain operations as described herein.

[0075] In various embodiments, computer hardware, such as a processing element, may be implemented as special purpose or as general purpose. For example, the processing element may comprise dedicated circuitry or logic that is permanently configured, such as an applicationspecific integrated circuit (ASIC), or indefinitely configured, such as an FPGA, to perform certain operations. The processing element may also comprise programmable logic or circuitry (e.g., as encompassed within a general -purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement the processing element as special purpose, in dedicated and permanently configured circuitry, or as general purpose (e.g., configured by software) may be driven by cost and time considerations.

[0076] Accordingly, the term “processing element” or equivalents should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which the processing element is temporarily configured (e.g., programmed), each of the processing elements need not be configured or instantiated at any one instance in time. For example, where the processing element comprises a general-purpose processor configured using software, the general-purpose processor may be configured as respective different processing elements at different times. Software may accordingly configure the processing element to constitute a particular hardware configuration at one instance of time and to constitute a different hardware configuration at a different instance of time.

[0077] The processing element may include processors, microprocessors (single-core and multi-core), microcontrollers, DSPs, field-programmable gate arrays (FPGAs), analog and / or digital application-specific integrated circuits (ASICs), or the like, or combinations thereof. The processing element may generally execute, process, or run instructions, code, code segments, software, firmware, programs, applications, apps, processes, services, daemons, or the like. The processing element may also include hardware components such as finite-state machines, sequential and combinational logic, and other electronic circuits that can perform the functions necessary for the operation of the current invention. The processing element may be incommunication with the other electronic components through serial or parallel links that include address busses, data busses, control lines, and the like.

[0078] Computer hardware components, such as communication elements, memory elements, processing elements, and the like, may provide information to, and receive information from, other computer hardware components. Accordingly, the described computer hardware components may be regarded as being communicatively coupled. Where multiple of such computer hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the computer hardware components. In embodiments in which multiple computer hardware components are configured or instantiated at different times, communications between such computer hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple computer hardware components have access. For example, one computer hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further computer hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Computer hardware components may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).

[0079] The memory device or element may include data storage components, such as readonly memory (ROM), programmable ROM, erasable programmable ROM, random-access memory (RAM) such as static RAM (SRAM) or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, optical disks, flash memory, thumb drives, universal serial bus (USB) drives, or the like, or combinations thereof. In some embodiments, the memory element may be embedded in, or packaged in the same package as, the processing element. The memory element may include, or may constitute, a “computer-readable medium”. The memory element may store the instructions, code, code segments, software, firmware, programs, applications, apps, services, daemons, or the like that are executed by the processing element.

[0080] The communication element may generally allow communication with systems and / or external devices. The communication element may include signal or data transmitting and receiving circuits, such as antennas, amplifiers, filters, mixers, oscillators, digital signal processors (DSPs), and the like. The communication element may establish communication wirelessly by utilizing RF signals and / or data that comply with communication standards such as cellular 2G,3G, 4G, 5G, or LTE, WiFi, WiMAX, Bluetooth®, BLE, or combinations thereof. The communication element may be in communication with the processing element and the memory element.

[0081] The position-detection device may comprise a global positioning system (GPS) device and / or real-time kinematic (RTK) technology for determining a position of relevant components.

[0082] The user interface generally allows the user to utilize inputs and outputs to interact with the device and is in communication with the one or more processing element. Inputs may include buttons, pushbuttons, knobs, jog dials, shuttle dials, directional pads, multidirectional buttons, switches, keypads, keyboards, mice, joysticks, microphones, or the like, or combinations thereof. The outputs of the present invention may include a display and / or any number of additional outputs, such as audio speakers, lights, dials, meters, printers, or the like, or combinations thereof, without departing from the scope of the present invention.

[0083] The various operations of example methods described herein may be performed, at least partially, by one or more processing elements that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processing elements may constitute processing element-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processing element-implemented modules.

[0084] Similarly, the methods or routines described herein may be at least partially processing element-implemented. For example, at least some of the operations of a method may be performed by one or more processing elements or processing element-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processing elements, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processing elements may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processing elements may be distributed across a number of locations.

[0085] Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer with a processing element andother computer hardware components) that manipulates or transforms data represented as physical (e g., electronic, magnetic, or optical) quantities within one or more memories (e g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0086] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0087] The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s).

[0088] Although the technology has been described with reference to the embodiments illustrated in the attached drawing figures, it is noted that equivalents may be employed and substitutions made herein without departing from the scope of the technology as recited in the claims.

[0089] Having thus described various embodiments of the technology, what is claimed as new and desired to be protected by Letters Patent includes the following:

Claims

CLAIMS1. A monitoring device for capturing data about a subject, the monitoring device comprising:a wireless power transfer receiver configured to convert wireless electromagnetic radiation into electrical energy supplied to one or more conductors;a micro-electromechanical system (MEMS) sensor operable to receive at least a portion of the electrical energy and generate one or more electrical signals based at least in part on one or more mechanical waves associated with the subject; and a transmitter configured to transmit one or more wireless signals based at least in part on the one or more electrical signals generated by the MEMS sensor.

2. The monitoring device of claim 1, further comprising one or more processing elements in communication with the MEMS sensor and configured to sample the one or more electrical signals generated by the MEMS sensor to produce sample data, wherein the one or more wireless signals are representative of the sample data.

3. The monitoring device of claim 2, wherein the one or more processing elements is configured to implement adaptive duty cycling based on at least one of an available radiofrequency energy, a sensor sampling requirement, or a transmitter communication requirement.

4. The monitoring device of claim 2, wherein the processing element is configured to transition between a low-frequency sampling mode in which the processing element samples the one or more electrical signals generated by the MEMS sensor at a default rate and a burstacquisition mode in which the processing element samples the one or more electrical signals generated by the MEMS sensor at a burst rate that is greater than the default rate for a predetermined duration.

5. The monitoring device of claim 4, wherein the default rate is equal to or less than one sample per hour, the burst rate is at least one hundred hertz, and the predetermined duration is at least thirty seconds.

6. The monitoring device of claim 2, wherein the MEMS sensor is configured to operate as a microphone sampling at a rate of at least two kilohertz, and the processing element is configured to extract vocalization features to quantify one or more of a distress call, a cough, or a respiratory sound event.

7. The monitoring device of claim 1, wherein the transmitter is configured to transmit ultra-high frequency signals comprising data packets with an advertising channel and data channels.

8. The monitoring device of claim 7, wherein the data packets comprise generic attribute profiles.

9. The monitoring device of claim 7, further comprising a power source configured to store electrical energy received from the wireless power transfer receiver, wherein the transmitter is configured to transmit the ultra-high frequency signals with a duty cycle at least partially based on available energy in the power source.

10. The monitoring device of claim 2, further comprising a visual indicator, wherein the one or more processing elements is configured to detect a cardiopulmonary trend and activate the visual indicator based at least partially on the cardiopulmonary trend.

11. The monitoring device of any one of the preceding claims, further comprising a housing that encloses at least a portion of the wireless power transfer receiver, the MEMS sensor, and the transmitter.

12. The monitoring device of claim 11, wherein the housing is an ear tag.

13. The monitoring device of claim 12, wherein the housing mechanically couples to an existing ear-button post to enhance retention and acoustic coupling.

14. The monitoring device of claim 11, wherein the housing is operable to be attached to a collar.

15. The monitoring device of claim 11, wherein the housing is operable to be attached to an ear tag.

16. The monitoring device of claim 11, wherein the housing is an intracervical insertion.

17. The monitoring device of any one of the preceding claims, wherein the MEMS sensor has a sensitivity of at least 73 fF / ps.

18. The monitoring device of any one of the preceding claims, wherein the MEMS sensor has a frequency response from DC to at least 10kHz.

19. The monitoring device of any one of the preceding claims, wherein the MEMS sensor has a noise floor less than Img / ^Hz.

20. The monitoring device of any one of the preceding claims, wherein the wireless power transfer receiver is a radio-frequency -to-direct-current power converter.

21. The monitoring device of any one of the preceding claims, wherein the wireless electromagnetic radiation has a frequency between about 902 MHz to about 928 MHz.

22. The monitoring device of any one of the preceding claims, wherein the wireless electromagnetic radiation has a frequency between about 2.4 GHz and about 2.485 GHz.

23. The monitoring device of any one of the preceding claims, wherein the wireless power transfer receiver has a power conversion efficiency greater than 60%.

24. The monitoring device of any one of the preceding claims, wherein the wireless power transfer receiver has an operating voltage range of about 2 V to about 3.6 V.

25. The monitoring device of any one of the preceding claims, further comprising one or more electrocardiogram (EKG) probes configured to capture EKG data, wherein the transmitter is configured to transmit one or more wireless signals representative of the EKG data.

26. The monitoring device of any one of the preceding claims, further comprising one or more pulse oximeters configured to capture oxygen saturation data, wherein the transmitter is configured to transmit one or more wireless signals representative of the oxygen saturation data.

27. The monitoring device of any one of the preceding claims, further comprising one or more accelerometers configured to capture movement data, wherein the transmitter is configured to transmit one or more wireless signals representative of the movement data.

28. The monitoring device of claim 27, wherein the one or more accelerometers comprises an inertial measurement unit.

29. The monitoring device of any one of the preceding claims, wherein the subject is at least one of a bovine, equine, ruminant, or camelid.

30. The monitoring device of any one of the preceding claims, wherein the data is representative of at least one of a heart rate, a heart rate variability, a murmur, an arrythmia, a lung sound, a cough, a burp, a movement of the subject, a reproduction mounting attempt related to the subject, an altered gait, a fetal heart rate, a fetal movement, a rumen contraction, a regurgitation, a parturition, or a cecal contraction.

31. A system for monitoring a subject, the system comprising:a monitoring device according to any one of the preceding claims;a wireless power transfer transmitter configured to transmit the wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device; and a receiver configured to receive the one or more wireless signals from the transmitter of the monitoring device.

32. The system of claim 31, further comprising one or more server processing elements in communication with the receiver and configured to receive the data representative of the one or more mechanical waves.

33. The system of claim 32, wherein the one or more server processing elements is configured to analyze the data representative of the one or more mechanical waves to determine at least one of a heart rate, a heart rate variability, a murmur, an arrythmia, a lung sound, a cough, a burp, a movement of the subject, a reproduction mounting attempt related to the subject, an altered gait, a fetal heart rate, a fetal movement, a rumen contraction, a regurgitation, a parturition, or a cecal contraction.

34. The system of claim 32, wherein the one or more server processing elements is configured to determine an amount of methane released by the subject based at least in part on the data.

35. The system of any one of claims 31-34, wherein the receiver is part of a transceiver configured to transmit one or more wireless signals representative of the data to a remote device.

36. The system of claim 35, wherein the transceiver is configured to transmit one or more wireless signals representative of an alert related to the data.

37. The system of any one of claims 31-36, further comprising one or more additional monitoring devices according to any one of claims 1-30, wherein the receiver is configured to receive one or more additional wireless signals from the one or more additional monitoring devices.

38. The system of any of claims 31-37, further comprising a mobile reading device comprising:a mobile wireless power transfer transmitter configured to transmit the wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device; anda receiver configured to receive the one or more wireless signals from the transmitter of the monitoring device.

39. The system of claim 38, further comprising a mobile vehicle operable to transport the mobile reading device.

40. The system of claim 39, wherein the mobile vehicle is an aerial drone.

41. The system of any one of claims 39 or 40, wherein the mobile vehicle is configured to autonomously patrol an area proximal to the subject so that the mobile reading device can obtain the one or more wireless signals from the transmitter of the monitoring device.

42. The system of claim 31, further comprising one or more additional monitoring devices according to any one of claims 1-30, wherein the receiver is configured to buffer data received from the monitoring device and the one or more additional monitoring devices and transmit the data via a low-power wide-area backhaul.

43. A method of capturing data of a subject, the method comprising:securing a monitoring device according to any of claims 1-30 to the subject; transmitting, via a wireless power transfer transmitter, wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device; and receiving, via a receiver, one or more wireless signals from the transmitter of the monitoring device.

44. The method of claim 42, further comprising receiving, via one or more server processing elements, the data representative of the one or more mechanical waves.

45. The method of claim 44, further comprising analyzing, via the one or more server processing elements, the data representative of the one or more mechanical waves to determine at least one of a heart rate, a heart rate variability, a murmur, an arrythmia, a lung sound, a cough, a burp, a movement of the subject, a reproduction mounting attempt related to the subject, an altered gait, a fetal heart rate, a fetal movement, a rumen contraction, a regurgitation, a parturition, or a cecal contraction.

46. The method of claim 44, further comprising determining, via the one or more server processing elements, an amount of methane released by the subject based at least in part on the data.

47. The method of any one of claims 42-46, wherein the receiver is part of a transceiver, further comprising transmitting, via the transceiver, one or more wireless signals representative of the data to a remote device.

48. The method of claim 47, further comprising transmitting, via the transceiver, one or more wireless signals representative of an alert related to the data.

49. The method of any one of claims 42-48, further comprising receiving, via the receiver, one or more additional wireless signals from one or more additional monitoring devices according to any one of claims 1-30.

50. The method of any of claims 42-49, further comprising:transmitting, via a mobile wireless power transfer transmitter of a mobile reading device, wireless electromagnetic radiation to the wireless power transfer receiver of the monitoring device; andreceiving, via a receiver of the mobile reading device, the one or more wireless signals from the transmitter of the monitoring device.

51. The method of claim 50, further comprising transporting, via a mobile vehicle, the mobile reading device.

52. The method of claim 51, wherein the mobile vehicle is an aerial drone.

57. The method of any one of claims 51 or 52, further comprising autonomously patrolling, via the mobile vehicle, an area proximal to the subject so that the mobile reading device can obtain the one or more wireless signals from the transmitter of the monitoring device.

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