System and Method for Learning Long-Distance Recognition and Personalization of Gestures

A system for gesture recognition comprising:

• a processor;
• and
• a non-transitory computer readable medium in data communication with the processor, the non-transitory computer readable medium having computer readable data including instructions stored thereon that when executed by the processor is configured to cause the processor to perform a method that comprises:
• training a machine learning system using a training dataset of sensor data that include a set of gestures, the training dataset including at least a first subset that displays a first gesture and a second subset that displays a second gesture;
• generating loss data based on a first loss function that includes a first cross entropy loss and a second cross entropy loss;
• updating parameters of the machine learning system based on the loss data;
• and
• outputting the machine learning system for gesture recognition of the set of gestures,
• wherein,
• the machine learning system includes (i) a first subnetwork to generate feature data based on the sensor data, (ii) a second subnetwork to extract a selected patch of the feature data, and (iii) a third subnetwork to generate gesture data based on a classification of the corresponding feature data of the selected patch,
• the first cross entropy loss is based on a first performance of the second subnetwork in relation to the training dataset, and
• the second cross entropy loss is based on a second performance of third subnetwork in relation to the training dataset.

A non-transitory computer readable medium having computer readable data including instructions stored thereon that, when executed by a processor, is configured to cause the processor to perform a method that comprises:

• training a machine learning system using a training dataset of sensor data that include a set of gestures, the training dataset including at least a first subset that displays a first gesture;
• generating loss data based on a first loss function that includes a first cross entropy loss and a second cross entropy loss;
• updating parameters of the machine learning system based on the loss data;
• and
• outputting the machine learning system for gesture recognition of the set of gestures,
• wherein,
• the machine learning system includes (i) a first subnetwork to generate feature data based on the sensor data, (ii) a second subnetwork to extract a selected patch of the feature data, and (iii) a third subnetwork to generate gesture data based on a classification of the corresponding feature data of the selected patch,
• the first cross entropy loss is based on a first performance of the second subnetwork in relation to the training dataset, and
• the second cross entropy loss is based on a second performance of third subnetwork in relation to the training dataset.