Abstract: A method, system and apparatus for touch gesture recognition are provided. A device generates a trajectory corresponding to a two-dimensional touch gesture. The device generates a plurality of variations of the trajectory in one or more of two dimensions. The device extracts one or more features of the trajectory and the plurality of variations of the trajectory. The device generates, from the one or more features, one or more machine learning classifiers. The device stores, at a memory, the one or more machine learning classifiers, such that a machine learning algorithm uses the one or more machine learning classifiers to recognize the two-dimensional touch gesture when receiving touch gesture input.

Abstract: A method of gesture detection in a controller includes; storing, in a memory connected with the controller: (i) a primary inference model definition corresponding to a plurality of gesture identifiers, and (ii) a set of auxiliary model definitions, each corresponding to a respective one of the gesture identifiers; obtaining, at the controller, motion sensor data; selecting a candidate gesture identifier from the plurality of gesture identifiers, based on the motion sensor data and the primary inference model definition; validating the candidate gesture identifier using the auxiliary model definition that corresponds to the candidate gesture identifier; and when the candidate gesture identifier is validated, presenting the candidate gesture identifier.

Abstract: A method of gesture detection in a controller includes: storing, in a memory connected with the controller, inference model data defining inference model parameters for a plurality of gestures; obtaining, at the controller, motion sensor data; extracting an inference feature from the motion sensor data; selecting, based on the inference feature and the inference model data, a detected gesture from the plurality of gestures; and presenting the detected gesture