Certain embodiments of the present disclosure relate to a monitoring device that includes a housing, contactless sensors coupled to the housing, and a processing device disposed in the housing. In at least one embodiment, the monitoring device is configured to measure pulse wave velocity in a subject.

Embodiment relate to a system for developing a model to classify continuous glucose monitoring (CGM) data. The system includes a processor and computer memory having instructions stored thereon that when executed will cause the processor to determine whether two CGM profiles match based on a similarity of shapes of the two CGM profiles, each CGM profile including a data set of CGM measurements. The processor designates two matching CGM profiles as a CGM profile pair. The processor transforms the CGM profile pair into a motif. The processor labels the motif as a labelled motif based on a clinical characteristic. The processor recursively repeats the determine, designate and transform steps of a CGM profile pairing process until a finite set of motifs is created, which includes the labelled motif as a classified data point. The processor monitor, analyzes, or influences a concentration of glucose levels in a fluid.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

Embodiments are disclosed for sleep staging using machine learning. In an embodiment, a method comprises: receiving, with at least one processor, sensor signals from a sensor, the sensor signals including at least motion signals and respiratory signals of a user; extracting, with the at least one processor, features from the sensor signals; predicting, with a machine learning classifier, that the user is asleep or awake based on the features; and computing, with the at least one processor, a sleep or wake metric based on whether the user is predicted to be asleep or awake.

Provided are foldable electronic device and method for estimating bio-information by using the same. The foldable electronic device may include: a main body part including a first main body and a second main body that are configured to be folded toward each other or unfolded from each other along a fold line where the first main body and the second main body meet; an image sensor part including a first image sensor and a second image sensor which are disposed at the first main body; and a processor configured to obtain a contact image of an object from the first image sensor disposed at the first main body and obtain an image of a marker that is displayed on the second main body, from the second image sensor disposed at the first main body, when the object is in contact with the first image sensor and the main body part is folded along the fold line, and estimate bio-information based on the contact image of the object and the image of the marker.

Provided is a wearable medical device that includes a processor or logic circuitry. The wearable medical device may include a memory storing instructions that, when executed by the processor or logic circuitry, configure the wearable medical device to determine, by the processor or the logic circuitry, that an event affecting a blood glucose measurement value trend of a user has occurred. Based on the occurrence of the event, the processor or the logic circuitry may select a mode of operation of the analyte sensor, and generate a signal indicating the selected mode of operation. The mode of operation may correspond to a sampling frequency of a physical attribute or physiological condition of a user of the wearable medical device.

An electronic device may include a display, a photoplethysmogram (PPG) sensor, a wireless communication circuit, a processor operatively connected to the display, the PPG sensor, and the wireless communication circuit, and a memory operatively connected to the processor. The electronic device implements the method, including monitoring blood glucose values of a user using the PPG sensor, displaying a notification prompting the user to measure blood glucose values using an external electronic device based at least partially on the monitored blood glucose values, and receive additional blood glucose values measured by the external electronic device using the wireless communication circuit.

Methods and systems for mapping boundaries of a given environment by a processor of a computer system, the method comprising: determining a trajectory of the body in the given environment over the given time period; and determining, based on the trajectory of the body in the given environment, one or more of an outer boundary of the given environment, and an inner boundary of the given environment. Methods and systems for mapping functionalities of a given environment executable by a processor of a computer system, the method comprising determining a pattern of movement of a body in the given environment in a given time period; and determining a functional identity of at least one zone in the given environment based on the pattern of movement of the body to obtain a mapped given environment.

A blood flow occlusion system includes an inflator and a pressure regulator coupled to a pressure cuff and a computing device coupled to the inflator and the regulator. The computing device comprises a memory coupled to the processor which is configured to be capable of executing programmed instructions stored in the memory to: determine brachial occlusion data based on calculating the brachial occlusion data with an occlusion regression equation using one or more input diagnostic parameters associated with a client; generate treatment parameters of a treatment program to treat the condition of the client based on the optimized brachial occlusion data and an obtained stage of the condition; generate programmed instructions of control commands to manage operation of the pressure cuff, inflator, and pressure regulator based on the treatment parameters; and initiate execution of the programmed instructions for the treatment program for treating the condition of the client when engaged.

Systems and methods for monitoring chronic over-pacing (COP) to the heart are discussed herein. In an embodiment, a system includes a receiver circuit to receive information about pacing rates of a plurality of paced heart beats, and a pacing analyzer circuit to generate a pacing rate distribution using pacing rates of the plurality of the paced heart beats. The pacing rate distribution includes a pacing rate histogram. The pacing analyzer circuit may recognize a morphological pattern from the pacing rate distribution, and detect a COP indication using the extracted feature. A programmer circuit adjusts one or more therapy parameters in response to the detected. COP indication.