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: Pattern matching apparatus for comparing signal samples with a pattern to find signal samples substantially matching said pattern, the apparatus comprising: a projector for projecting said signal samples over a tree of projection vectors of successively increasing frequency at nodes of said tree to successively provide projection values, a distance measurer, associated with an output of said projector for determining a distance between a projection value and a corresponding projection value of said pattern, said distance being an expression of a minimum bound of a true distance between said sample and said pattern, and a thresholder, associated with an output of said distance measurer for applying a threshold to said minimum bound to successively reject samples at respective nodes.

Abstract: Pattern matching apparatus for comparing signal samples with a pattern to find signal samples substantially matching said pattern, the apparatus comprising: a projector for projecting said signal samples over a tree of projection vectors of successively increasing frequency at nodes of said tree to successively provide projection values, a distance measurer, associated with an output of said projector for determining a distance between a projection value and a corresponding projection value of said pattern, said distance being an expression of a minimum bound of a true distance between said sample and said pattern, and a thresholder, associated with an output of said distance measurer for applying a threshold to said minimum bound to successively reject samples at respective nodes.