The present disclosure provides a sleep monitoring garment and a sleep monitoring system. The sleep monitoring garment includes a wearable textile structure; a first monitoring band circumferentially extending along the wearable textile structure; a second monitoring band circumferentially extending along the wearable textile structure and with a distance to the first monitoring band; and an interface communicatively coupled with the first monitoring band and the second monitoring band.

An arousal level prediction apparatus and method are disclosed. The arousal level prediction apparatus obtains first biological information indicating current biological information of the user, obtains first environment information indicating a current environment around the user, and obtains living information of the user indicating an activity history of the user. The arousal level predication apparatus includes a process that calculates a first arousal level indicating a current arousal level of the user based on the first biological information, predicts a second arousal level, which is an arousal level of the user at a certain period of time later, based on the first arousal level, the first environment information and the living information, and outputs the second arousal level.

In a configuration in which a holder holding a controller is mounted on a seat part with a plate-shaped member, the exposure of the mounting part of the plate-shaped member on which the holder is mounted is eliminated. A seat device includes a pressure sensor measuring a value relating to the seated person's state, a vibration imparting device a vibration imparting operation, an ECU controlling the vibration imparting device corresponding to the measurement result of the pressure sensor, a holder holding the ECU, and a mounting bracket fixed to a lower frame such that the holder is mounted on the lower frame of a seat part. The mounting bracket includes a mounting projection on which a side wall of the holder is mounted in a predetermined mounting direction. When the side wall is mounted on the mounting projection, the mounting projection is covered with the side wall.

A system and method for conducting a test includes initiating a test script, and providing the script to a patient. Testing hardware is shipped hardware from a provider to the patient, and instructions are provided to the patient for connecting the testing hardware to a mobile computing device. A mobile application is downloaded and installed onto the mobile computing device. The testing hardware is connected to the testing hardware and to the mobile computing device, and the test is conducted using the mobile application operating on the mobile computing device.

A percussive therapy system that includes a percussive massage device including a network interface, and an intelligence engine. The intelligence engine is configured to receive manual capture data and real-time tracking data from the percussive massage device, receive remote data from a remote data source, and generate recommendation data comprising a recommended protocol to be performed by the percussive massage device. The recommendation data is generated from demographic, activity, temporal, analytics, and biometric data, received from the manual capture data, the real-time tracking data, and the remote data inputs.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

Multifunctional telemetry apparatus for real-time emergency support comprising biosensors set of electrical biosensors set, green LEDs, red LEDs, infrared LEDs, near infrared LEDs and photodetector set and temperature biosensor, accelerometer, pressure sensor set and wireless antenna, which enables the life-support network to infer and monitor user's present condition through a plurality of real-time biological information, clinical emergency conditions, movement data and events of victimizer unstrapping the apparatus. The embodiments of the multifunctional telemetry apparatus to track emergency situations through analysis of tracking information even in moving conditions or victimizer based emergency situation without active inputs or active involvement from user. Classified clinical emergency and child tracking applications synchronized to the emergency support apparatus, and these applications operated by the life-support having classified trigger commands and tracking information displayed in a manner to help efficiently mitigate emergency risks. The apparatus configured through novel communication means to communicate to life-support network even with no access to standard communication channels.

Disclosed herein is a device for continuous physiological monitoring as well as systems and methods for interpreting data from such a device. The systems and methods may include automatically detecting, assessing, and analyzing exercise activity, physical recovery states, sleep states, and the like. The acquisition of continuous physiological data may facilitate automated recommendations concerning changes to sleep, recovery time, exercise routines, and the like.

A method includes (i) receiving data associated with a user during a sleep session; (ii) determining an alertness level of the user using a machine learning model that takes as input the received data; and (iii) generating a response to be communicated to the user based at least in part on the determined alertness level. The data associated with the user can be received from a respiratory therapy device configured to supply pressurized air to an airway of the user by way of a user interface coupled to the respiratory therapy device via a conduit, a sensor, or both the respiratory therapy device and the sensor.