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 method for estimating a likelihood of a stroke condition of a subject, the method comprising: acquiring clinical measurement data pertaining to said subject, said clinical measurement data including at least one of image data, sound data, movement data, and tactile data; extracting from said clinical measurement data, potential stroke features according to at least one predetermined stroke assessment criterion; comparing said potential stroke features with classified sampled data acquired from a plurality of subjects, each positively diagnosed with at least one stroke condition, defining a positive stroke dataset; and determining, according to said comparing, a probability of a type of said stroke condition, and a probability of a corresponding stroke location of said stroke condition with respect to a brain location of said subject.

Abstract: A method for estimating a likelihood of a stroke condition of a subject, the method comprising: acquiring clinical measurement data pertaining to said subject, said clinical measurement data including at least one of image data, sound data, movement data, and tactile data; extracting from said clinical measurement data, potential stroke features according to at least one predetermined stroke assessment criterion; comparing said potential stroke features with classified sampled data acquired from a plurality of subjects, each positively diagnosed with at least one stroke condition, defining a positive stroke dataset; and determining, according to said comparing, a probability of a type of said stroke condition, and a probability of a corresponding stroke location of said stroke condition with respect to a brain location of said subject.