A health management apparatus includes a processor that is configured to activate a sugoroku including squares linking a starting square with a finishing square and direct a piece of a user to be advanced through the squares. The processor is configured to obtain measurement data including biological information of the user and a time of day or location of measurement at which the biological information has been measured. The processor is configured to assess an evaluation value based on whether the time of day or location of measurement satisfies a condition, and determine a number of squares through which the piece is to be advanced in the sugoroku, based on the evaluation value.

A health management apparatus includes a processor that is configured to activate a sugoroku including squares linking a starting square with a finishing square and direct a piece of a user to be advanced through the squares. The processor is configured to obtain measurement data including biological information of the user and a time of day or location of measurement at which the biological information has been measured. The processor is configured to assess an evaluation value based on whether the time of day or location of measurement satisfies a condition, and determine a number of squares through which the piece is to be advanced in the sugoroku, based on the evaluation value.

The present invention provides a carotid blood pressure detection device, comprising: a first sensing unit, a second sensing unit, and a controller connected or coupled to the first sensing unit and the second sensing unit. The first sensing unit is disposed on a subject's neck and adjacent to a first position of the subject's carotid arteries. The second sensing unit is disposed on the subject's neck and adjacent to a second position of the subject's carotid arteries. The controller derives a mean arterial pressure of a section of the subject's carotid arteries that lies between the first position and the second position of the subject's carotid arteries from pulse wave data measured and obtained by the first sensing unit and pulse wave data measured and obtained by the second sensing unit.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct measurement.

A method for gating in tomographic imaging system includes steps of: (a) performing a tomographic imaging on an object with a target moving periodically along a first axis for acquiring projection images; (b) obtaining projected curves by summing up pixel values along a direction of a second axis perpendicular to the first axis in each projection image; (c) determining a target zone on the projection images, wherein a central position on the first axis of the target zone is corresponding to a position having the largest variation in the projected curves on the first axis; (d) calculating parameter values of pixel values in the target zones and obtaining a curve of a moving cycle of the target according to the parameter values; and (e) selecting the projection images under the same state in the moving cycle for image reconstruction according to the curve of the moving cycle of the target.

A device is configured to process medical-event related information, such as associated with a medical code event, such as by receiving medical-event related information, recording medical event information with timestamps, generating additional medical information, displaying medical events and additional medical information on media display, visual indicators, and user devices to provide real-time notifications of recorded and reminders for upcoming medical events, and sending medical events with timestamps and additional medical information to other devices for further processing. The device may be configured as a code clock which includes an analog clock.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The present disclosure provides a coupling system for coupling a medical accessory to an accessory support is provided. The present disclosure also provides a system for powering a medical accessory with an accessory support.

The present disclosure provides a coupling system for coupling a medical accessory to an accessory support is provided. The present disclosure also provides a system for powering a medical accessory with an accessory support.

The disclosure relates to a method and a tracking system for tracking a medical object. Herein, image data obtained by an imaging method and a predetermined target position is acquired for the medical object. The image data is used to detect the medical object automatically by an image processing algorithm and track the position thereof in a time-resolved manner. Furthermore, it is furthermore indicated when, or that, the detected medical object has reached the target position. A plurality of the detected positions of the medical object and associated detection times are stored in a database.