A mirror therapy device includes a first positioning assembly, a second positioning assembly and a transmission assembly connecting the first and second positioning assemblies. The first and second positioning assemblies are arranged in opposition to each other in such a manner that both are able to reciprocate. The first positioning assembly is configured for positioning of a healthy limb of a patient, and the second positioning assembly is configured for positioning of an affected limb of the patient. The transmission assembly is configured for power transmission between the first and second positioning assemblies so that the second positioning assembly moves in synchronization with the first positioning assembly under the action of the transmission assembly. A mirror therapy system includes the mirror therapy device and a display device. The mirror therapy system enables the healthy limb to drive movement of the affected limb to allow the affected limb to repeat the same movements as the healthy limb, thus establishing neural connections for synchronous vision, motor perception and proprioception of the patient's healthy and affected sides and better stimulating nerve recovery of the patient.

A system comprising processing circuitry configured to receive a wirelessly-transmitted message from a medical device, the message indicating that the medical device detected an acute health event of the patient. In response to the message, the processing circuitry is configured to determine a location of the patient, determine an alert area based on the location of the patient, and control transmission of an alert of the acute heath event of the patient to any one or more computing devices of one or more potential responders within the alert area.

A system and method for performing range of motion measurements in an unsupervised manner. A user equipment with one or more cameras may be used with an application on the UE using the one or more cameras to measure range of motion of a user of the UE. The patient may record movement of the part of the body of interest, such as the head. The movement may be recorded in three dimensions (e.g., calculated from two dimensional coordinates of an image) and processed by the application, and the three rotational angles calculated corresponding to the difference between the final position of the head and the initial position of the head at the beginning of the recording. As a result, facial recognition is used to improve monitoring of range of motion during surgical recovery in an unsupervised environment.

Devices, systems, and methods are provided for analyzing and treating learning disorders using software as a medical device. A method may include identifying, by a device, application-based cognitive musical training (CMT) exercises associated with performance of software; receiving a first user input to generate a first sequence of the application-based CMT exercises; presenting a first application-based CMT exercise of the application-based CMT exercises based on the first sequence; receiving, during the presentation of the first application-based CMT exercise, a second user input indicative of a user interaction with the first application-based CMT exercise; generating, based on a comparison of the second user input to a performance threshold, a second sequence of the application-based CMT exercises, the first sequence different than the second sequence; and presenting a second application-based CMT exercise of the application-based CMT exercises based on the second sequence.

Aspects relate to systems and methods for individualized content media delivery. An exemplary system includes a sensor configured to detect a biofeedback signal as a function of a biofeedback of a user, a display configured to present content to the user, and a computing device configured to control an environmental parameter for an environment surrounding the user as a function of the biofeedback signal, wherein controlling the environmental parameter additionally includes generating an environmental machine-learning model as a function of an environmental machine-learning algorithm, training the environmental machine-learning model as a function of an environmental training set, wherein the environmental training set comprises biofeedback inputs correlated to environmental parameter outputs and generating the environmental parameter as a function of the biofeedback signal and the environmental machine-learning model.

Disclosed are an electronic device for generating workout types and a method of operating the electronic device. According to an embodiment, an electronic device includes: a memory and at least one processor connected to the memory, wherein the at least one processor is configured to: acquire a workout type name in response to a request for adding a workout type, recommend at least one data type based on the workout type name, generate a new workout type corresponding to the workout type name based on a data type selected from among the at least one recommended data type, and acquire workout measurement information corresponding to the selected data type in response to execution of the new workout type.

Embodiments of the present disclosure provide systems and methods for providing an audio user interface by which a user may be provided audio instructions for measuring physiological parameters using a monitoring device as well as an audio explanation of the results of analysis of the physiological parameters. A cloud service may receive via a smart device a voice command associated with measuring physiological parameters and provide to the smart device, instructions for measuring the physiological parameters. The monitoring device is configured to communicate with the cloud service via the smart device, which may provide the instructions for taking the ECG using the ECG monitoring device to a user as an audio output. The cloud service may receive physiological parameter data measured by the monitoring device and process the data to provide a set of interpretations of the data as well as audio data corresponding to a simplified explanation thereof.

A walking support system is a walking support system which supports walking of a user, and includes a display unit, a landing timing detection unit configured to detect landing at the time of walking of the user, a target landing timing setting unit configured to set a target landing timing of the user on the basis of an output of the landing timing detection unit, and a display control unit configured to cause the display unit to display an auxiliary image prompting the user to land at the target landing timing.

Techniques and systems include predicting various health conditions using a photoplethysmography (PPG) signal or a video signal based on images of a patient's fingertip or other body portion captured using a mobile device. The video signal may be transformed into a pseudo PPG signal to measure blood volume changes in the patient's blood flow to derive data indicating a disease state or health-related characteristic, such as blood oxygen level, blood glucose level, heart rate variability, hemoglobin, respiration rate, or arrhythmia. Techniques involve real-time environment assessment and problematic issue detection, training an artificial intelligence (AI) model to measure signal quality so as to select high-quality signals from a range of signals, and domain adaption and transfer learning to make use of publicly available datasets.

A system includes a minimally intrusive display system (MIDS) configured to be disposed on an eyewear. The MIDS includes a display system and a sensor system configured to provide for a sensor data. The MIDS further includes a processor configured to process the sensor data to derive a physiological measure. The processor is further configured to display, via the display system, the physiological measure, wherein the display system is disposed in the eyewear so that the physiological measure is only viewed when a user of the eyewear turns the user's pupil towards the display system at angle α from a forward direction.