Data augmentation of motion trajectories and synthesis of em signatures for ml-based human gesture recognition and activity detection

An electronic device, comprising:

• a processor, configured to:
• receive motion capture data of a target;
• generate a first set of motion trajectories from the motion capture data;
• generate a first set of augmented motion trajectories using a set of data augmentation functions on the first set of motion trajectories;
• generate a radar cross-section of the target using the motion capture data to perform at least one of gesture recognition or activity detection;
• generate one or more synthetic electromagnetic (EM) signatures of one or more activities of the target using the first set of augmented motion trajectories and the radar cross-section;
• and
• train a machine learning model configured for EM signature-based gesture recognition or activity detection with a domain adaptation process using the one or more synthetic EM signatures.

A non-transitory computer-readable medium comprising program code, that when executed by at least one processor of an electronic device, causes the electronic device to:

• receive motion capture data of a target from a camera;
• generate a first set of motion trajectories from the motion capture data;
• generate a first set of augmented motion trajectories using a set of data augmentation functions on the first set of motion trajectories;
• generate a radar cross-section of the target using the motion capture data to perform at least one of gesture recognition or activity detection;
• generate one or more synthetic electromagnetic (EM) signatures of one or more activities of the target using the first set of augmented motion trajectories and the radar cross-section;
• and
• train a machine learning model configured for EM signature-based gesture recognition or activity detection with a domain adaptation process using the one or more synthetic EM signatures.