Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

A method can include receiving (1) images of at least one subject and (2) at least one total mass value for the at least one subject. The method can further include executing a first machine learning model to identify joints of the at least one subject. The method can further include executing a second machine learning model to determine limbs of the at least one subject based on the joints and the images. The method can further include generating three-dimensional (3D) representations of a skeleton based on the joints and the limbs. The method can further include determining a torque value for each limb, based on at least one of a mass value and a linear acceleration value, or a torque inertia and an angular acceleration value. The method can further include generating a risk assessment report based on at least one torque value being above a predetermined threshold.

There is provided a device for assisting exercise, comprising a video providing unit configured to provide a first video data including a first exercise movement, a data obtaining unit configured to obtain a second video data based on an input relating to the first video data, a joint information extracting unit configured to extract a first joint information obtained by detecting plural skeletons from the second video data, an analyzing unit configured to obtain an analysis information based on a similarity determined by comparing the first joint information with a second joint information of the first video data and a recommendation unit configured to obtain recommendation information for recommending an exercise movement to a user based on at least one of the first video data, the second video data and the analysis information from a database including plural exercise movements may be provided according to an embodiment.

An electronic device includes an information acquisition unit, a display information determination unit, and a display control unit. The information acquisition unit acquires first pulse wave information indicating a pulse wave of a part of a body based on image information of the body in a first image obtained by imaging at least the part of the body, and acquires second pulse wave information indicating a pulse wave of a part of the body based on image information of the body in a second image obtained by imaging the part of the body after the first image has been obtained. The display information determination unit determines a display range of a display color of a display image or brightness corresponding to a blood flow variation of a part of the body based on first pulse wave information and second pulse wave information. The display control unit controls display of the display image by the display color determined based on the display range or brightness and the second pulse wave information.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and second branch pose estimation neural network; (iii) generating a first training signal from the second branch pose estimation neural network using a three dimensional reconstruction module for input into the first branch pose estimation neural network; (iv) generating one or more volumetric heatmaps; and (v) applying a maximization function to the one or more volumetric heatmaps to obtain a 3D pose of one or more persons in the scene.

A method for measuring respiratory parameters of a subject using a range imaging sensor, wherein the method includes: receiving from the range imaging sensor at least one raw image of at least one portion of the torso of the subject, wherein each point of the raw image represents the distance between the range imaging sensor and the subject; generating a surface image of at least one portion of a surface of the torso of the subject by surface interpolation of the raw image; estimating a respiratory signal as a function of time calculated as the spatial average, in a given region of interest (ROI) defined on the torso of the subject, of the differences between the depth values of the surface image at a given time and the depth values of a reference surface image; and estimating a lung volume.

Methods and systems for assessing characteristics of a lung of a patient. A method includes, during an applied positive end expiratory pressure, identifying a first position of a patient, acquiring first impedance data representative of at least a region of patient's lung when the patient is in the first position, during the applied positive end expiratory pressure, identifying a second different position of a patient, acquiring second impedance data representative of at least the region of patient's lung when the patient is in the second position, comparing the first impedance data with the second impedance data, and determining whether the applied positive end expiratory pressure is sufficient to effectuate recruitment.

A positioning method of functional rotation center of shoulder based on rigid upper arm model includes: step 1: abstracting a human upper arm into a cylinder with FRCS as a center of top surface; step 2: determining a reference axis vector of the cylinder; step 3: determining an axis vector of the cylinder and a displacement from the reference axis vector to the axis vector; step 4: correcting a central axis direction of the cylinder; step 5: determining a height compensation of the cylinder, and positioning the FRCS. The method has higher accuracy for the positioning result of FRCS, the positioning result of FRCS has better stability relative to the upper arm and trunk, and can be used to establish a more accurate human digital dynamic model and predict more accurate human posture.

Methods and systems are provided for detecting patient motion during a diagnostic scan. In one example, a method for a medical imaging system includes obtaining output from one or more patient monitoring devices configured to monitor a patient before and during a diagnostic scan executed with the medical imaging system, receiving a request to initiate the diagnostic scan, tracking patient motion based on the output from the one or more patient monitoring devices, and initiating the diagnostic scan responsive to patient motion being below a threshold level.

A machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.