A computer-implemented method for human gait analysis based on a video stream obtained from a monocular camera device, the video stream comprising a plurality of frames reflecting the walk of a human individual, the method comprising: inferring, from the obtained video stream, three-dimensional gait information wherein the three-dimensional gait information includes estimates of the individual's joint locations including at least the individual's foot locations on each frame, the estimates being derived by matching for each frame two-dimensional joint coordinates of the respective frame with respective three-dimensional model information of the individual's body; determining one or more gait parameters of the individual based on the individual's foot locations in local extrema frames showing local extrema of the distance between one foot location of the individual and a corresponding reference joint location; characterized in that inferring further comprises: deriving heat-maps and location-maps for joint location estimation by using a convolutional neural network trained on three dimensional human pose datasets, wherein a particular heat-map describes, for a corresponding frame, probability values that a particular joint is associated with respective pixels of the corresponding frame, and wherein a particular set of location-maps includes a plurality of location-maps, with each of the location maps describing the distance of a particular joint to a root location for the corresponding frame in a respective spatial dimension; receiving a selection of at least a frame sequence of the video stream during which the joints of the individual move smoothly over time; estimating a skeleton model of the individual by determining, for each frame of the selected sequence, a loss for each joint of a default skeleton model in each spatial coordinate, and adjusting the default skeleton model to compensate the determined losses to provide an adjusted skeleton model; and performing kinematic skeleton fitting per video frame using the adjusted skeleton model to determine a plurality of joint locations including at least the foot locations of the individual's feet on each frame. inferring, from the obtained video stream, three-dimensional gait information wherein the three-dimensional gait information includes estimates of the individual's joint locations including at least the individual's foot locations on each frame, the estimates being derived by matching for each frame two-dimensional joint coordinates of the respective frame with respective three-dimensional model information of the individual's body; determining one or more gait parameters of the individual based on the individual's foot locations in local extrema frames showing local extrema of the distance between one foot location of the individual and a corresponding reference joint location; characterized in that inferring further comprises: deriving heat-maps and location-maps for joint location estimation by using a convolutional neural network trained on three dimensional human pose datasets, wherein a particular heat-map describes, for a corresponding frame, probability values that a particular joint is associated with respective pixels of the corresponding frame, and wherein a particular set of location-maps includes a plurality of location-maps, with each of the location maps describing the distance of a particular joint to a root location for the corresponding frame in a respective spatial dimension; receiving a selection of at least a frame sequence of the video stream during which the joints of the individual move smoothly over time; estimating a skeleton model of the individual by determining, for each frame of the selected sequence, a loss for each joint of a default skeleton model in each spatial coordinate, and adjusting the default skeleton model to compensate the determined losses to provide an adjusted skeleton model; and performing kinematic skeleton fitting per video frame using the adjusted skeleton model to determine a plurality of joint locations including at least the foot locations of the individual's feet on each frame. deriving heat-maps and location-maps for joint location estimation by using a convolutional neural network trained on three dimensional human pose datasets, wherein a particular heat-map describes, for a corresponding frame, probability values that a particular joint is associated with respective pixels of the corresponding frame, and wherein a particular set of location-maps includes a plurality of location-maps, with each of the location maps describing the distance of a particular joint to a root location for the corresponding frame in a respective spatial dimension; receiving a selection of at least a frame sequence of the video stream during which the joints of the individual move smoothly over time; estimating a skeleton model of the individual by determining, for each frame of the selected sequence, a loss for each joint of a default skeleton model in each spatial coordinate, and adjusting the default skeleton model to compensate the determined losses to provide an adjusted skeleton model; and performing kinematic skeleton fitting per video frame using the adjusted skeleton model to determine a plurality of joint locations including at least the foot locations of the individual's feet on each frame.