Abstract: In various examples there is a method of generating training data. The method comprising: receiving a video frame captured from each of a plurality from different viewpoints of a subject performing an activity. The method comprises generating a 3D point cloud from the video frames and applying colour information from the video frames to the 3D point cloud to produce a textured 3D model. The 3D model is modified to produce a modified 3D model. The method generates a synthetic image of the textured 3D point cloud by positioning a virtual camera at a specified angle, height and distance from the modified 3D model. The method repeats the operation of generating the synthetic image for a plurality of different virtual camera angles, heights and distances from the modified 3D model.

Abstract: A method for managing a communications network comprises receiving, as input to a multi-armed bandit, a decision instance defining a task for managing the communications network and environmental data. An arm of the multi-armed bandit is selected, wherein each arm comprises a model of a respective action from a plurality of possible actions that can be taken in response to the received decision instance. For the selected arm, execution of the associated action on the communications network is triggered to produce an outcome. The observed outcome of the selected arm is compared with an outcome predicted by the model of the selected arm. Using the comparison determine whether to update the model of the selected arm with a historic model, wherein the historic model defines a previously determined relationship between the action corresponding to the selected arm, the received decision instance and the environmental data.

Abstract: Sedentary behaviours of a person may be monitored for well-being. Captured sensor data depicting at least one sedentary person, and values of configuration parameters of a capture device used to capture the sensor data are received. The captured sensor data is processed to compute skeletal data of the person. The skeletal data is input to a classifier to obtain classification data, for a plurality of classes related to behaviour of the person. The configuration parameters are stored in association with the classification data and patterns are detected in the stored classification data. Values of the configuration parameters are adjusted using the detected patterns.