The wearable article (200) comprises a sensing component. The electronics module (100) is removably coupled to the wearable article (200). The electronics module comprises a housing and a processor disposed within the housing (101). An interface element (121, 123) interfaces with the sensing component so as to receive signals from the sensing component and provide the same to the processor. A sensor (105) is disposed within the housing (101). The sensor (105) monitors a property of the environment external the electronics module (100) through the housing (101). The housing (101) is constructed such that the sensor (105) has line of sight through the housing (101).

An example method includes receiving a request to create a surgical case corresponding to a patient identifier and a provider identifier and associating one or more of patient data and provider data with the surgical case based on the patient identifier or the provider identifier. Case data is retrieved from a medical management system using one or more of the patient identifier and the provider identifier to query the medical management system. A workflow state for the surgical case is initialized by setting statuses for preconfigured workflow items based on the case data. A view of the surgical case is displayed via a user interface. The view of the surgical case displays at least a portion of the case data and includes one or more status elements configured based on the workflow state for the surgical case.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

A system includes: a light source that emits pulsed light that illuminates a user's head portion; a photodetector that detects at least part of pulsed light returning from the head portion and that outputs one or more signals corresponding to an intensity of the at least part; electrical circuitry; and a memory that stores an emotion model indicating a relationship between the one or more signals and emotions. Based on a change in the one or more signals, the electrical circuitry selects an emotion by referring to the model. The one or more signals include a first signal corresponding to an intensity of first part of the reflection pulsed light and a second signal corresponding to an intensity of second part of the reflection pulsed light. The first part includes part before a falling period is started; and the second part includes at least part in the falling period.

Described herein are devices and methods for continuous real time monitoring of kidney function. In various embodiments, a urine analysis device collects sensor data describing one or more properties of urine. The urine analysis device may be integrated with a catheter system to continuously generate sensor data in real time as the urine is collected by the catheter system. Sensor data collected by the urine analysis device may be analyzed by physicians to detect changes in a patients kidney function. If necessary, based on the sensor data, physicians may perform an intervention to improve a patients kidney function.

Controlled-environment facility resident behavioral and/or health monitoring may employ controlled-environment facility resident wearables each having a band configured to be affixed around a portion of a controlled-environment facility resident, irremovable by the resident and may include sensor(s) configured to measure biometric(s) of the controlled-environment facility resident and one or more physical parameter(s) experienced by the wearable, with a transmitter transmitting the biometric(s) and/or the physical parameter(s) to a controlled-environment facility management system. The controlled-environment facility management system may predetermine one or more normal input levels of the biometric(s) and/or physical parameter(s), receive the transmitted biometric(s) and/or physical parameter(s), determine whether received biometric(s) and/or physical parameter(s) rises above or falls below the predetermined normal input level(s), and alert controlled-environment facility personnel and/or law enforcement when received physical parameter(s) and/or received biometric(s) rise above or fall below the predetermined normal input level(s).

An apparatus device may include a bio-impedance sensor configured to take a bio-impedance measurement from a body of an individual, an optical sensor configured to take an optical measurement from the body of the individual, and a processing device configured to receive a first bio-impedance measurement from the bio-impedance sensor taken during a first period of time and a first optical measurement from the optical sensor taken during the first period of time, receive first location information of the individual during the first period of time, determine a first correlation between a physiological parameter and at least one of the first location, the first bio-impedance measurement, or the first optical measurement, and determine a first level of the physiological parameter based on the first correlation.

A system and method is provided for monitoring a biological substance in a bodily orifice. The system includes a wearable device configured to be worn in a bodily orifice. A biosensor is carried by the wearable device and is constructed and arranged to obtain raw data regarding a biological substance in the orifice. The biosensor includes a processor circuit to provide processed data from the raw data, and a transmitter to wirelessly transmit the processed data to a second device.

A method and system are provided for estimating a physiological parameter using a parameter model determined by a deep neural network. An example method includes training a deep neural network with indirect and direct physiological parameters from a user database. The medical parameters include a respiratory rate, oxygen saturation, temperature, blood pressure, and pulse rate. The method includes determining if a new user belongs in a group. If the parameter model estimated physiological parameter using the closest group to the new user and associated calibration, then the method quantizes the parameter inputs to determine which physiological parameter a new user is most sensitive and to determine a new group and calibration coefficients or curves for the new user.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI/machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI/machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.