A removable wearable device sleeve includes a sleeve body, one or more sensors, and an interface for operative communication with the wearable device. A removable sleeve includes a sleeve body having a front element having a first aperture, a back element having a second aperture, a tubular element having a first end and a second end with a third aperture, and one or more back elements define a cavity to encase an earpiece, an interface, and a sensor, wherein the sensor communicates sensor readings to the wearable device through the interface. A method of placing a sensor configured to communicate with a wearable device onto the wearable device includes providing a removable sleeve and inserting the wearable device through the first aperture of the front element into the cavity of the removable sleeve to provide a removable wearable device sleeve.

Described herein are systems, devices, and methods to assess and correct for instability of baseline pressure of pressure sensors applied for measuring pressures inside a human body or body cavity, such as intracranial pressure (ICP) and arterial blood pressure (ABP). The present disclosure includes systems for assessing instability of baseline pressure by computing differences in single pressure wave parameters between single pressure waves, calculating pressure stability levels, determining differences between pressure stability levels and creating baseline pressure indicator plots. The baseline pressure indicator plots define instability of baseline pressure as a function of defined thresholds applied to parameters of the pressure stability levels. The disclosure also provides means for correcting mean pressure caused by baseline pressure instability.

A method for determining the average usage of an analyte monitoring system may include the step of calculating one or more analyte measurements of a host during a first period of time. The method may include the step of calculating a duration of time that the host uses the analyte monitoring system during the first period of time. The method may include the step of calculating an average time that the host uses the analyte monitoring system during a second period of time or a percentage of the first period of time that the host uses the analyte monitoring system, in which the average time or the percentage is calculated based on at least the calculated duration. The method may include the step of generating and displaying a report indicating the average time or the percentage.

The disclosed system includes a user-mountable electronic device, an output interface, and at least one processor. The electronic device includes a housing and at least one sensor device located within the housing and configured to generate sensor output that indicates orientation or motion of the user-mountable electronic device. The at least one processor is operated to: receive the sensor output; identify, based on the received sensor output, a body part on which the user intends to deploy the user-mountable electronic device; determine a preferred orientation of the user-mountable electronic device relative to the identified body part; and cause the output interface to provide deployment guidance that indicates the preferred orientation of the user-mountable electronic device.

A method for determining the average usage of an analyte monitoring system may include the step of calculating one or more analyte measurements of a host during a first period of time. The method may include the step of calculating a duration of time that the host uses the analyte monitoring system during the first period of time. The method may include the step of calculating an average time that the host uses the analyte monitoring system during a second period of time or a percentage of the first period of time that the host uses the analyte monitoring system, in which the average time or the percentage is calculated based on at least the calculated duration. The method may include the step of generating and displaying a report indicating the average time or the percentage.

Systems, devices, and methods are provided for changing the power state of a sensor control device in an in vivo analyte monitoring system in various manners, such as through the use of external stimuli (light, magnetics) and RF transmissions.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

A medical system includes a physiological monitoring system configured to sense a physiological signal and record physiological signal data indicative of the patient's physiological state. The physiological monitoring system including a controller, a storage device, at least one sensor operatively coupled to the controller, and a first communication component. The system includes a mobile device configured to facilitate sensor placement, the mobile device comprising a controller, a display device, and a second communication component configured to facilitate communication between the physiological monitoring system and the mobile device. The controller of the mobile device is configured to provide a graphical user interface (GUI) on the display device, the GUI including information about a proper placement of the at least one sensor, wherein the proper placement is determined based on the physiological signal data.

An IoT device and system improves on prior approaches to tracking and analyzing symptoms and behavior of special needs individuals. The IoT device may include one or more buttons that can be pressed or held for an extended time by the user. Button presses and duration may be registered in a remote computer as being associated to a symptom or behavior. Patterns may be registered based on a sequence of the button presses and duration. Triggered button events may be collected and transmitted to a host server/network for re-transmission to support team members. In some embodiments, a machine learning module may analyze the collected data for patterns and/or deviations of behavior, which may be displayed to team members. In some embodiments, an automatic prognosis may be generated.

A home automation (HA) system may include at least one HA operation device that includes a user-settable thermostat for controlling a heating, ventilation, and air-conditioning (HVAC) system associated with a living area of a user. The HA system may also include a controller. The controller may be configured to collect user thermostat setting data from the user-settable thermostat, and use machine learning to identify a health condition of the user based upon the user thermostat setting data.