Abstract: A computer-implemented method for predicting risk of fall of a user is disclosed. The method includes: receiving temporal data related to a skeleton of the user, from at least two sensors; reconstructing from the temporal data at least one of, a temporal 3D scene reconstruction, and a temporal three-dimensional (3D) skeleton reconstruction; extracting spatio-temporal features from the 3D scene reconstruction or the 3D skeleton reconstruction; introducing at least one spatio-temporal feature to a machine-learning (ML) model, wherein said ML model is trained to predict fall probability of a user based on said spatio-temporal feature; and predicting fall probability of the user based on an output of the ML model.

Abstract: A system comprising at least two three-dimensional (3D) cameras that are each configured to produce a digital image with a depth value for each pixel of the digital image; and a processor configured to: perform inter-camera calibration by: (i) estimating a pose of a subject, based, at least in part, on a skeleton representation of a subject captured each of by said at least two 3D cameras, wherein said skeleton representation identifies a plurality of skeletal joints of said subject, and (ii) enhancing the estimated pose based, at least in part, on a 3D point cloud of a scene containing the subject, as captured by each of said at least two 3D cameras, and perform data merging of digital images captured by said at least two 3D cameras, wherein the data merging is per each of said identifications.

Abstract: A system comprising at least two three-dimensional (3D) cameras that are each configured to produce a digital image with a depth value for each pixel of the digital image; and a processor configured to: perform inter-camera calibration by: (i) estimating a pose of a subject, based, at least in part, on a skeleton representation of a subject captured each of by said at least two 3D cameras, wherein said skeleton representation identifies a plurality of skeletal joints of said subject, and (ii) enhancing the estimated pose based, at least in part, on a 3D point cloud of a scene containing the subject, as captured by each of said at least two 3D cameras, and perform data merging of digital images captured by said at least two 3D cameras, wherein the data merging is per each of said identifications.

Abstract: A system comprising at least two three-dimensional (3D) cameras that are each configured to produce a digital image with a depth value for each pixel of the digital image; and a processor configured to: perform inter-camera calibration by: (i) estimating a pose of a subject, based, at least in part, on a skeleton representation of a subject captured each of by said at least two 3D cameras, wherein said skeleton representation identifies a plurality of skeletal joints of said subject, and (ii) enhancing the estimated pose based, at least in part, on a 3D point cloud of a scene containing the subject, as captured by each of said at least two 3D cameras, and perform data merging of digital images captured by said at least two 3D cameras, wherein the data merging is per each of said identifications.

Abstract: A method for estimating the location and orientation of a mobile robot with respect to landmarks whose positions are given using a sensor for measuring bearings of the landmarks with respect to the robot, where bias errors are present. The method comprises using the sensor to obtain bearings the landmarks. The location and orientation of the robot with respect to the landmarks is estimated, based on the measured bearings, and bias errors associated with the measured bearings are corrected to obtain corrected bearings. A correction function is used to finds the minimal sum of squared errors between measured bearings and the corrected bearings. The location and the orientation of the robot is determined to be where the sum of squared errors between measured bearings and the corrected bearings is minimal.

Abstract: A method for estimating the location and orientation of a mobile robot with respect to landmarks whose positions are given using a sensor for measuring bearings of the landmarks with respect to the robot, where bias errors are present. The method comprises using the sensor to obtain bearings the landmarks. The location and orientation of the robot with respect to the landmarks is estimated, based on the measured bearings, and bias errors associated with the measured bearings are corrected to obtain corrected bearings. A correction function is used to finds the minimal sum of squared errors between measured bearings and the corrected bearings. The location and the orientation of the robot is determined to be where the sum of squared errors between measured bearings and the corrected bearings is minimal.