Method and system for symmetric recognition of handed activities

A method of training a machine learning model, the method comprising:

• identifying training instances associated with a first asymmetry, wherein each training instance is associated with a corresponding class label and a corresponding segment of activity data;
• for each training instance,
• modifying that training instance by -
• transforming the corresponding class label to a class label of a second asymmetry opposite the first asymmetry, and
• transforming the corresponding segment of activity data;
• and
• training the machine learning model using the modified training instances,
• wherein as a result of said modifying, none of the modified training instances are labeled with the first asymmetry.

A non-transitory medium with instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising:

• identifying training instances associated with a first asymmetry, wherein each training instance is associated with a corresponding class label and a corresponding segment of activity data;
• for each training instance,
• modifying that training instance by -
• transforming the corresponding class label to a class label of a second asymmetry opposite the first asymmetry, and
• transforming the corresponding segment of activity data;
• and
• training a machine learning model such that one or more weights are parameterized based on the modified training instances and the corresponding transformed segments of activity data.