Various embodiments of a physiological waveform summarizing system of the present disclosure encompass a clinical physiological waveform (CPW) interface (20), and a physiological waveform summarizing monitor (30). In operation, the monitor (30) extracts a set of dominant physiological templates (41) from a clinical physiological waveform (CPW) communicated by the interface (20) to the monitor (30). The set of dominant physiological templates (41) represent a dominating major physiological rhythm (DMPR) of the clinical physiological waveform (CPW) temporally spanning over consecutive intervals of the clinical physiological waveform (CPW), and each dominant physiological template (41) is derived from a different interval of the consecutive intervals of the clinical physiological waveform (CPW). The monitor (30) may extract one or more secondary physiological templates (42) representative of secondary major physiological rhythm(s) (SMPR) present in the clinical physiological waveform (CPW), and provide a diagnostic major physiological rhythm log of the clinical physiological waveform (CPW) including a diagnostic plotting of the dominant physiological templates (41) and any secondary physiological template(s) (42).

The present disclosure generally relates to cycle tracking. A notification for a respective recurrence of the recurring event is displayed. Representations of days are displayed with affordances for initiating processes for recording information corresponding to various days.

Systems and methods to determine a risk factor related to dehydration and thermal stress of a subject are disclosed. Exemplary implementations may: generate output signals, by one or more sensors worn on a body of a subject, conveying information related to one or more of location of the subject, motion of the subject, temperature of the subject, cardiovascular parameters of the subject; store information related to the subject; obtain the output signals; determine in an ongoing manner, from the output signals, values of a water loss metric that correlates with estimated percentage of bodyweight of the subject lost in water; obtain heat index information for a contextual environment surrounding the subject; determine in an ongoing manner, from the output signals, values of an exertion metric that correlates with exertion of the subject due to work; and determine in an ongoing manner values for an aggregated risk factor of the subject.

Systems and methods are provided remotely controlling a medical device. In some embodiments, systems and methods are also provided for remote medical monitoring. This includes, for example, emergency/panic notifications/functions, medical event recording, compliance monitoring, sleep timer and environmental controls, two-way communication, and other functions such as, for example, emergency telephony/communication in various forms. In other embodiments, systems and methods for managing a remote control of a medical device are provided. This includes, for example, two-way communication for assisting in locating the remote, power management including sleep mode and wireless charging, and master remote/key functionality. The remote can be handheld or wearable and may include, for example, audio, visual, haptic, input, communication, and sensor (including biosensor) functionality and outputs. In this manner, the remote control can not only control the medical device, but also provides the user with extended functionality for emergency and non-emergency communication and tasks.

A method for evaluating a posture of a rider riding a bicycle includes: continuously receiving a plurality of sensor datasets, each of the sensor datasets being associated with a specific time instance and includes data generated by an inertial measurement set and an electrical signal sensor set; determining a plurality of top time instances and a plurality of bottom time instances, and establishing a number of riding periods based on the plurality of top time instances and the plurality of bottom time instances; and for each of the riding periods, generating an evaluation result with respect to a number of sensor datasets received within the riding period.

A robot according to the present disclosure comprises: a microphone; a camera disposed to face a predetermined direction; and a processor configured to: inactivate driving of the camera and activate driving of the microphone, if a driving mode of the robot is set to a user monitoring mode; acquire a sound signal through the microphone; activate the driving of the camera based on an event estimated from the acquired sound signal; confirm the event from the image acquired through the camera; and control at least one constituent included in the robot to perform an operation based on the confirmed event.

A healthcare apparatus includes a ballistocardiogram (BCG) sensor configured to sense a ballistocardiogram signal of a subject, a camera configured to acquire a color facial image, and a processor configured to detect a region of interest (ROI) from the color facial image, to detect a first color image of a forehead area to acquire a first black and white image, to detect a second color image of a cheek area to acquire a second black and white image, to apply the first and second black and white images to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject, to calculate a first heart rate from the BCG signal waveform, to calculate a second heart rate from the remote PPG signal waveform, and to output a heart rate of the subject based on the first heart rate and the second heart rate.

A method for estimating a likelihood of a stroke condition of a subject, the method comprising: acquiring clinical measurement data pertaining to said subject, said clinical measurement data including at least one of image data, sound data, movement data, and tactile data; extracting from said clinical measurement data, potential stroke features according to at least one predetermined stroke assessment criterion; comparing said potential stroke features with classified sampled data acquired from a plurality of subjects, each positively diagnosed with at least one stroke condition, defining a positive stroke dataset; and determining, according to said comparing, a probability of a type of said stroke condition, and a probability of a corresponding stroke location of said stroke condition with respect to a brain location of said subject.

A portable electrocardiographic device configured to measure an electrocardiographic waveform using a plurality of types of lead systems includes an electrode unit configured to be brought into contact with a subject's body and measure an electrocardiographic waveform, an analysis unit configured to analyze the electrocardiographic waveform measured by the electrode unit in accordance with a lead system at a time of measurement of the electrocardiographic waveform, a storage unit configured to store the electrocardiographic waveform measured at the electrode unit, the lead system, and an analysis result of the electrocardiographic waveform analyzed by the analysis unit in association with one another, and a remeasurement facilitating unit configured to prompt a user, when the analysis result or a state of the measured electrocardiographic waveform satisfies a predetermined condition, for remeasurement in a predetermined lead system different from the lead system at the time of the measurement of the electrocardiographic waveform.

The present disclosure discloses an intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data. The key point of the technical solution is as follows: The intelligent screening algorithm and automatic upgrading system includes a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module; the heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing; and the heart sound data processing module is configured to process the data in the heart sound data module and extract heart sound feature parameters.