A method comprising receiving an input indicating intake information associated with a patient. Based on the input, the method further includes determining an initial discharge date and receiving mobility information associated with the patient. Based in part on the mobility information, the method further includes determining an estimated discharge date and a confidence metric associated with the estimated discharge date, determining that the estimated discharge date is later than the initial discharge date by more than a threshold period of time, and determining that the confidence metric is greater than a threshold metric. Based in part on the estimated discharge date being later than the initial discharge date by more than the threshold period of time and the confidence metric being greater than the threshold metric, the method further includes generating an alert.

A system for connecting a first ship to a second ship, the system having a plurality of target items coupled to the second ship, a camera module coupled to the first ship and configured to provide information comprising positions of images of the target items in a FOV, and a processor coupled to the camera module and a memory and configured to determine a first position and a first orientation of the second ship relative to the first ship.

The presented invention includes the generation of cloud points, the identification of objects in the cloud points, and, in this case, finding the positions of objects in cloud points. In addition, the invention includes capturing images, data streaming, and digital image processing in different points of the system, and calculation of the position of objects. The invention includes the usage of cameras of mobile smart devices, smart glasses, 3D cameras, but not necessarily. The data streaming provides video streaming and sensor data streaming from mobile smart devices. The presented invention further includes cloud points of buildings in which the positioning of separated objects could be implemented. It also consists of the database of cloud points of isolated objects which help to calculate the position in the building. Finally, the invention comprises the method of objects feature extraction, comparing in the cloud points and position calculation.

A system configured to assist a user in scanning a physical environment in order to generate a three-dimensional scan or model. In some cases, the system may include an interface to assist the user in capturing data usable to determine a scale or depth of the physical environment and to perform a scan in a manner that minimizes gaps.

A system and method for high-confidence error overbounding of multiple optical pose solutions receives a set of candidate correspondences between 2D image features captured by an aircraft camera and 3D constellation features including at least one ambiguous correspondence. A candidate estimate of the optical pose of the camera is determined for each of a set of candidate correspondence maps (CMAP), each CMAP resolving the ambiguities differently. Each candidate pose estimate is evaluated for viability and any non-viable estimates eliminated. An individual error bound is determined for each viable candidate pose estimate and CMAP, and based on the set of individual error bounds a multiple-pose containment error bound is determined, bounding with high confidence the set of candidate CMAPs and multiple pose estimates where at least one is correct. The containment error bound may be evaluated for accuracy as required for flight operations performed by aircraft-based instruments and systems.

A system, tracking an object within an environ, includes a hub with a processor instantiating a vision recognizer, a data store storing location data including a first location model, and a first camera which captures and provides, within a first field of view (FOV), a first image to the hub. The first image includes a first depiction of a first captured location, and the vision recognizer determines an object is present at the first location when the first depiction of the first captured location corresponds to the first location model. The first image depicts an element present within the first FOV, the data store further stores an image library record including a first object model, and the vision recognizer determines whether the object is present at the first location when the element depicted in the first image corresponds to the object as modeled by the first object model.

Systems, apparatuses, devices and methods for body pose tracking are provided that are simple, inexpensive, flexible, accurate and robust. One body pose tracking system includes a mobile device, such as a smartphone, and active or passive marker bands. Images and depth information captured by the smartphone may be analyzed using an Inverse Kinematic (IK) model, and, in certain cases, the IK model solution may be augmented by machine learning. Other body pose tracking systems include an augmented-reality/virtual-reality (AR/VR) head-mounted-display (HMD) and/or AR/VR glasses rather than a smartphone. An AR/VR HMD device may include a depth sensor and multiple environment-facing cameras.

A system for event prediction using schema networks includes a first antecedent entity state that represents a first entity at a first time; a first consequent entity state that represents the first entity at a second time; a second antecedent entity state that represents a second entity at the first time; and a first schema factor that couples the first and second antecedent entity states to the first consequent entity state; wherein the first schema factor is configured to predict the first consequent entity state from the first and second antecedent entity states.

Aspects include a system and method for detection of computer-aided design (CAD) objects in point clouds. An example method includes obtaining, by a processing device, a labeled data set. The method further includes training, by the processing device, a model on the labeled data set using a two-dimensional (2D) object detector to calculate a three-dimensional (3D) box out of a detected 2D box by mapping coordinates on a geometric primitive image into 3D. The method further includes fitting, by the processing device, a computer-aided design (CAD) model into the 3D box.

A method includes receiving an image of a real environment captured using a camera worn by a user, the image comprising a hand of the user and determining a pose of the hand based on the image. Based on a three-dimensional model of the hand having the determined pose, generating a two-dimensional surface representing the hand as viewed from a first viewpoint of the user and positioning the two-dimensional surface representing the hand and one or more virtual-object representations in a three-dimensional space. The method further includes determining that a portion of the two-dimensional surface representing the hand is visible from a second viewpoint in the three-dimensional space, and generating an output image, wherein a set of image pixels of the output image corresponding to the portion of the two-dimensional surface that is visible is configured to cause a display to tur off a set of corresponding display pixels.