A system for monitoring the respiratory activity of a subject, which comprises one or more movement sensors, applied to the thorax of a subject, for generating first signals that are indicative of movement of the thorax of the subject; a receiver for receiving the first generated signals during breathing motion of the subject; and one or more computing devices in data communication with the receiver, for analyzing the breathing motion. The computing device is operable to generate a first breathing pattern from the first signals; divide each respiratory cycle experienced by the subject and defined by the first pattern into a plurality of portions, each of the portions delimited by two different time points and calculate, for each of the plurality of portions of a given respiratory cycle of the first pattern, a slope representing a thorax velocity; derive, from the given respiratory cycle of the first pattern, a pulmonary air flow rate of the subject during predetermined portions of the respiratory cycle; compare between corresponding portions of the first pattern and average flow rates during different phases of the breathing cycle, to calibrate a thorax velocities of the subject with pulmonary air flow rates; and determine respiratory characteristics of the subject for subsequent respiratory cycles experienced by the subject, based on a calculated thorax velocity and the calibration.

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.

An apparatus for estimating a core body temperature of an object is provided. According to one embodiment, the apparatus includes a plurality of sensors configured to obtain data from an object and a processor configured to obtain a surface temperature, a heat flux, a skin blood flow rate, and a blood flow velocity by using the data obtained from the plurality of sensors, obtain a skin thermal conductivity based on the skin blood flow rate, and estimate a core body temperature of the object based on the surface temperature, the heat flux, the skin thermal conductivity, and the blood flow velocity of the object.

A thermal imaging extremity abnormal perfusion detector system includes a computer processor configured to receive, analyze and store thermal images and a thermal imaging camera communicatively coupled to the processor, and configured to take at least one of photograph and video thermal images and output the thermal images to the processor. The camera is configured to be secured adjacent a patient workspace that is shaped to contain a patient; and points the thermal imaging camera at the workspace such that, responsive to taking at least one thermal image, the at least one thermal image contains the patient who is placed within the workspace. The computer processor is configured to analyze the at least one thermal image and determine from the at least one thermal image a difference in thermal states indicating altered perfusion in an extremity of the patient.

The present invention provides for a new system and method for diagnosing skin cancer that provides for a more effective analysis of changes in skin tissue due to the duality of acquiring visual data and transforming such visual into an audio signal. The conversion of complicated patterns of visual information of a skin lesion by a computer aided classification analysis into diagnostic sounds results in a much higher resolution rate and increased precision of diagnosis.

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.

An apparatus for estimating a core body temperature of an object is provided. The apparatus may include a heat flow sensor configured to measure a first heat flux from an object, a first temperature sensor positioned under a thermal conducting material and configured to measure a surface temperature of the object, a second temperature sensor positioned above the thermal conducting material and configured to measure a surface temperature of the thermal conducting material, and a processor configured to obtain a second heat flux by calibrating the first heat flux based on the surface temperature of the object and the surface temperature of the thermal conducting material, and configured to estimate a core body temperature of the object based on the obtained second heat flux and the surface temperature of the object.

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.

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.