The present invention relates generally to the field of providing personalized health management to users, particularly for users suffering from obesity and obesity-related medical conditions, in order to enhance weight loss through a smart belt that is optimally positioned around the user's waist, wherein the placement is determined at least in part using machine learning techniques on aggregate user data. The invention facilitates weight loss and mindfulness through feedback delivered via the smart belt.

An ECU device which estimates a length of a lower limb of a seated person who is seated in a vehicle seat is shown. The ECU device includes the following. A thigh angle information obtainer obtains information regarding a thigh angle of the seated person. A back knee angle information obtainer obtains information regarding a back knee angle of the seated person. An estimator estimates the length of the lower limb of the seated person based on the information regarding the thigh angle obtained by the thigh angle information obtainer and information regarding the back knee angle obtained by the back knee angle information obtainer.

A system comprising a machine-readable storage medium storing at least one program and a computer-implemented method for collecting body measurements of a human user using a body measurement garment is provided. The body measurement garment comprises a plurality of sensors configured to produce a set of output data. The method may include receiving the set of output data from the body measurement garment and determining a plurality of body measurements from the output data. The method may further include generating a body shape model representing the shape of the body of the user. The method may also include generating a garment fit model representing the fit of a garment on the human user.

The present disclosure provides methods of improving structure of a face in a patient, more particularly by classifying the facial shape in order to allow the design of a specific treatment plan directed to each type of face shape.

A medical system and method are disclosed herein. The medical system, in an embodiment, has computer-readable instructions configured to be executed by one or more processors to cause at least one display device of a wearable device to generate a plurality of graphics. The graphics are configured to stimulate a voluntary eye function of at least one eye of a subject, to stimulate an involuntary eye function of the at least one eye, and to block a vision of the at least one eye. The instructions are also configured to cause at least one sensor of the wearable device to sense eye movement, head movement, and pupillary resizing. The instructions are also configured to cause processing of a plurality of sensed eye parameters and any sensed head movement parameters and to generate an examination output that at least indicates a plurality of the sensed eye parameters.

Systems and methods for determining the height of a subject include a fixed unit mounted to a surface in electronic communication with a mobile unit permitting user input to drive instructions for the system. A sensor in the fixed unit emits a signal upon activation which is received. Time difference between emitting and receiving the signal is used to calculate the height of the room for calibrating the system and the height of the subject when measuring. The mobile unit includes a measurement surface positioned proximate to a maximal point of the subject for measurement, which intercepts and preferably reflects a measurement signal emitted by the sensor of the fixed unit. A light source in the fixed unit may create a mark on the support substrate indicating the location for the subject for measurement. The height determination may be sent to a hub or subject's EMR.

Automated modular physical health testing systems and associated devices and methods are disclosed herein. A modular system configured in accordance with embodiments of the present technology can include, for example, a housing, a communications hub, and a plurality of physical health testing devices. The housing integrates the communications hub and stores the plurality of physical health testing devices. The physical health testing devices are in wired and/or wireless communication with the communications hub. Each physical health testing device is configured to generate physical health data of a user and to transmit generated physical health data to the communication hub and/or a user's mobile device. The modular physical health testing system provides an automated physical exam that can be performed at user's homes or other convenient locations.

A biological information acquisition device includes a detection-value acquisition unit to acquire a detection value from a non-contact biometric sensor, a vital measurement unit to measure a vital sign of a target person (TP) using the detection value, an image-data acquisition unit to acquire image data indicating an image captured by a camera, an image processing unit to perform at least one of a state estimation process of estimating a state of the target person, an attribute estimation process of estimating an attribute of the target person, or a personal identification process of identifying the target person by performing image processing on the image captured including the target person, and a parameter setting unit to set a parameter in measuring the vital sign in accordance with a result of the image processing.

To provide a technology that can easily acquire body weight information of a patient. A processing part that deduces the body weight of a patient based on a camera image of the patient lying on a table of a CT device, including a generating part that generates an input image based on the camera image, and a deducing part that deduces the body weight of the patient when the input image is input into a learned model. The learned model is generated by a neural network executing learning using a plurality of learning images C1 to Cn generated based on a plurality of camera images, and a plurality of correct answer data G1 to Gn corresponding to the plurality of learning images C1 to Cn, where each of the plurality of correct answer data G1 to Gn represents a body weight of a human included in a corresponding learning image.

An image processing device includes an input/output interface, and at least one processor. The at least one processor executes: detecting feature points in a facial image of an object included in an image taken from the input/output interface; acquiring first distance information between the feature points before a beauty treatment is performed; acquiring second distance information between the feature points at a second timing after the beauty treatment is performed; acquiring a difference value between the first distance information and the second distance information; and determining whether the beauty treatment is correctly given or has been given based on a different value between the acquired distance information.