Systems and methods may include a first monitoring device positioned at a first location to monitor a monitored environment; and a second monitoring device positioned at a second location to monitor the monitored environment, wherein the first and second monitoring devices calibrate each other based on respective fiducial markers; wherein the first and second monitoring devices each comprise a processor and a memory coupled to the processor, the memory storing a plurality of instructions, which when executed by the processor, cause the processor to perform the operations of: detecting presence of the individual in the monitored environment; determining whether the condition of interest of the individual exceeds a determined threshold; based on the condition of interest of the individual exceeding the determined threshold, transmitting the condition of interest to a remote server to determine at least one of a social distancing breach and an identification of another person whom the individual contacted.

Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

According to some embodiments, a wearable medical device capable of treating a patient presenting with syncope is provided. The wearable medical device includes a memory storing event profile information, a battery, at least one treatment electrode coupled to the battery, at least one processor coupled to the memory and the at least one treatment electrode, and an event manager executed by the at least one processor. The event manager is configured to detect an event associated with syncope; store, in the memory, data descriptive of the event in association with an indication that the data includes data descriptive of a syncopal event; and address the event.

Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

A system and method for providing health care diagnosis, and more specifically, to a system and method for providing automated health condition review and diagnosis with testing recommendations is disclosed. The system includes a condition analyzer server coupled to online prior medical records sources, a current treating facility processor for generating a set of current patient data, the current patient data comprises observations, conditions, vital signs, and complaints, and a data storage device containing one or more sets of one or more predetermined criteria from reference sources and record results. The condition analyzer server return a report containing recommendations potential diagnosis, additional testing recommendations, and reference source materials utilized in generating the potential diagnosis, and additional testing recommendations to the current treating facility.

A surgical system for intra-operative and post-operative measurement of motion and position of the musculoskeletal system. The surgical system comprises a surgical navigation system configured to support the installation of at least one prosthetic component. The surgical navigation system measures motion and position of the musculoskeletal system during surgery. Holes are drilled in a first bone and a second bone of the musculoskeletal system to retain tracking devices of the surgical navigation system. Leaving the holes in the first bone and the second bone after completing the surgery can introduce stress risers that could reduce bone integrity. The surgical system includes a first implantable device and a second implantable device each having anchors corresponding to the holes of the first bone and the second bone. The first implantable device and the second implantable device each has an IMU to measure motion and position post-operatively. The anchors are configured to reduce stress risers.

Disclosed herein is a waste detection system. The waste detection system includes a pad configured to acquire a fluid sample excreted from a patient. The pad includes one or more layers and at least one microfluidic channel configured to receive therein the fluid sample. The waste detection system further includes an intake manifold in fluid communication with the at least one microfluidic channel, the intake manifold configured to receive the fluid sample, the intake manifold having one or more reagents configured to detect the presence of waste within the fluid sample excreted from the patient.

An intelligent defecation device for living creature includes a device body, a supporting portion, an image module, and a first analysis module. The supporting portion is formed within the inner side of the device body for accommodating a moisture absorption member so as to allow the living creature to leave over its excrement therein. The image module is also arranged at the device body for dynamically capturing the images of the excrement in the supporting portion and outputting the image. The first analysis module is arranged in the device body and connected with the image module to analyze and calculate the defecation mode with the image based on preset or accumulated data, so as to generate a signal when an abnormal defecation mode is diagnosed.

Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation. Audio stream data can be acquired from the second farm animal operation in a second farm animal operation monitoring event, and processed using the second machine learning library information to determine whether the sound type of interest is present in the acquired audio stream data, thereby generating information associated with the presence or absence of the condition during the second farm animal operation monitoring event.

A system for reversing inflammation in a user, wherein the system further comprises a computing device further designed and configured to receive a physiological extraction of a user, wherein the physiological extraction contains at least an inflammation marker, generate, using an inflammation machine-learning model and the user physiological extraction input, a first inflammation metric of a user, wherein generating the first inflammation metric further comprises training an inflammation machine-learning model as a function of inflammation metric training data, wherein the inflammation metric training data includes a plurality of entries, and each entry correlates user physiological extraction data to at least an inflammation metric that quantitates hallmarks of inflammation in the user, identify, using an alimentary element machine-learning process and the first inflammation metric, at least an alimentary element for reversing inflammation in the user, and provide, to the user, the at least an alimentary element for reversing inflammation.