US 12,333,432 B2
Method and system for symmetric recognition of handed activities
Colin Brown, Saskatoon (CA); and Andrey Tolstikhin, Montreal (CA)
Assigned to Hinge Health, Inc., San Francisco, CA (US)
Filed by Hinge Health, Inc., San Francisco, CA (US)
Filed on May 3, 2023, as Appl. No. 18/311,825.
Application 18/311,825 is a continuation of application No. 17/593,270, granted, now 11,657,281, previously published as PCT/IB2020/052249, filed on Mar. 12, 2020.
Claims priority of application No. CA 3036836 (CA), filed on Mar. 15, 2019.
Prior Publication US 2023/0274145 A1, Aug. 31, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01)
CPC G06N 3/08 (2013.01) [G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06V 40/107 (2022.01); G06V 40/20 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of training an activity classifier, the method comprising:
identifying training instances associated with a first handedness, 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 handedness opposite the first handedness, and
transforming the corresponding segment of activity data using a transformation that is associated with the first handedness;
and
training the activity classifier using the modified training instances,
wherein as a result of said modifying, none of the modified training instances are labeled with the first handedness.