Systems and methods to provide remote patient monitoring for viral-respiratory symptoms, including coronavirus or COVID-19 symptoms are disclosed, including a signal receiver circuit configured to receive first and second physiologic information of a patient, the first physiologic information comprising respiration rate information of a patient and the second physiologic information different than the first physiologic information, and an assessment circuit configured to determine an indication of patient viral-respiratory disease using the received first and second physiologic information.

Medical devices for use along the biliary and/or pancreatic tract. An example system for treating a stricture, for example along the biliary and/or pancreatic tract, may include a guidewire for antegrade stricture crossing along the biliary and/or pancreatic tract. The guidewire may have a distal end region and a proximal end region. A hub may be coupled to the proximal end region. A tubular sheath may be slidably disposed along the guidewire. The tubular sheath may have a plurality of slots formed therein. The hub may be configured to secure the axial position of the tubular sheath relative to the guidewire.

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.

The invention provides a method of analysing the consistency of stool, including the steps of: providing stool of an infant, capturing, with a portable device comprising a camera, an image of the stool, providing the captured image to an input layer of a pre-trained convolutional neural network, CNN, processing the captured image using the CNN to obtain, from a final layer of the CNN, a classification vector and to obtain information about a predicted score from the classification vector, wherein at least the final layer of the CNN has been customized so that each element of the classification vector corresponds to a score of a stool analysis scale, and storing information about the predicted store.

A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

Processes, scales, and devices to measure and quantify wetness perception in humans. Exemplary devices and scales utilize sensor fusion of temperature and pressure modalities, for which humans have dedicated receptors in the skin, to understand how the perception of wetness comes about. Processes test the utility of wetness perception as a biomarker for assaying peripheral neuropathy. Wetness perception devices include a Peltier module. The temperature of the Peltier module can be varied precisely using a computer-aided feedback system, mounted on a load scale to enable concomitant pressure measurements. Devices may include an insulation chamber with desiccators in place to lower internal humidity and prevent condensation. Wetness perception can be used as a non-invasive biomarker for disease-related peripheral neuropathy in which sensory mechanisms are disrupted.

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.

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.

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.

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.