Transducer-based systems can be configured to display a graphical representation of a transducer-based device, the graphical representation including graphical elements corresponding to transducers of the transducer-based device, and also including between graphical elements respectively associated with a set of the transducers and respectively associated with a region of space between the transducers of the transducer-based device. Selection of graphical elements and/or between graphical elements can cause activation of the set of transducers associated with the selected elements. Selection of a plurality of graphical elements and/or between graphical elements can cause visual display of a corresponding activation path in the graphical representation. Visual characteristics of graphical elements and between graphical elements can change based on an activation-status of the corresponding transducers. Activation requests for a set of transducers can be denied if it is determined that a transducer in the set of transducers is unacceptable for activation.

Techniques are provided for tracking heartrate metrics using different operating modes associated with different contexts of a wearable device. For example, a heartrate sensor of the wearable device may be operated in a first operating mode when an activity is being tracked within an application session of a particular application. The heartrate sensor may be operated in a second operating mode after detecting conclusion of the activity within the activity session (e.g., during sedentary time).

A physiological information processing apparatus includes a processor and a memory storing computer-readable instructions. When the instructions are executed by the processor, the physiological information processing apparatus obtains physiological information data indicative of physiological information of a subject, obtains a first parameter associated with a vital sign of the subject based on the physiological information data, displays a first trend graph showing temporal change in the first parameter in a first display area of a display screen of a display, obtains a second parameter associated with an autonomic nerve function of the subject based on the physiological information data, and displays a second trend graph showing temporal change in the second parameter in a second display area of the display screen. The first and second display areas are displayed next to each other such that time axes of the first and second display areas are synchronized with each other.

A bed has a mattress. A sensor is configured to sense sleep readings of a user sleeping on the bed. An automation controller is configured to transmit the sleep readings to a cloud service. Hardware hosting the cloud service is configured to compare the sleep readings with trend data in order to generate adjustments to the user's sleep environment.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

The present disclosure includes a handheld processing device including medical applications for minimally and noninvasive glucose measurements. In an embodiment, the device creates a patient specific calibration using a measurement protocol of minimally invasive measurements and noninvasive measurements, eventually creating a patient specific noninvasive glucometer. Additionally, embodiments of the present disclosure provide for the processing device to execute medical applications and non-medical applications.

A physiological monitoring device includes: a sensor interface, a display configured to display information related to the patient, and at least one processor. The at least one processor is configured to: operate the physiological monitoring device into a non-transport mode while docked to one of at least one monitor mount; display first location context information corresponding to a first patient care area on the display while the physiological monitoring device is operating in the non-transport mode in the first patient care area; detect an undocking event in response to undocking the physiological monitoring device from a first monitor mount of the at least one monitor mount, wherein the first monitor mount is located in the first patient care area; and in response to detecting the undocking event, operate the physiological monitoring device in a transport mode, including changing the first location context information to transport context information on the display.

The embodiments include an apparatus used in combination with a computer for sensing biopotentials. The apparatus includes a catheter in which there is a plurality of sensing electrodes, a corresponding plurality of local amplifiers, each coupled to one of the plurality of sensing electrodes, a data, control and power circuit coupled to the plurality of local amplifiers, and a photonic device bidirectionally communicating an electrical signal with the data, control and power circuit. An optical fiber optically communicated with the photonic device. The photonic device bidirectionally communicates an optical signal with the optical fiber. An optical interface device provides optical power to the optical fiber and thence to the photonic device and receives optical signals through the optical fiber from the photonic device. The optical interface device bidirectionally communicates an electrical data, control and power signal to the computer.

Systems, methods, and devices of a health device network may include: a non-invasive glucometer that non-invasively measures analyte levels; an invasive glucometer communicatively coupled directly to the non-invasive glucometer; a cloud-based server communicatively coupled to the non-invasive glucometer or the invasive glucometer; a user device communicatively coupled to the cloud-based server; and/or a user interface that displays the invasive glucose measurement, the non-invasive glucose measurement, a data batch, and/or processed data to the user. The non-invasive glucometer and/or the invasive glucometer may aggregate an invasive glucose measurement and a non-invasive glucose measurement into the data batch. A data analytics application on the cloud-based server may be configured to: integrate the invasive glucose measurement and the non-invasive glucose measurement; identify a correlation between the invasive glucose measurement and the non-invasive glucose measurement; and/or generate a predictive model based on the invasive glucose measurement and the non-invasive glucose measurement.

Systems, methods, and devices of a health device network may include: a non-invasive glucometer that non-invasively measures analyte levels; an invasive glucometer communicatively coupled directly to the non-invasive glucometer; a cloud-based server communicatively coupled to the non-invasive glucometer or the invasive glucometer; a user device communicatively coupled to the cloud-based server; and/or a user interface that displays the invasive glucose measurement, the non-invasive glucose measurement, a data batch, and/or processed data to the user. The non-invasive glucometer and/or the invasive glucometer may aggregate an invasive glucose measurement and a non-invasive glucose measurement into the data batch. A data analytics application on the cloud-based server may be configured to: integrate the invasive glucose measurement and the non-invasive glucose measurement; identify a correlation between the invasive glucose measurement and the non-invasive glucose measurement; and/or generate a predictive model based on the invasive glucose measurement and the non-invasive glucose measurement.