US 12,249,015 B2
Joint rotation inferences based on inverse kinematics
Dongwook Cho, Pierrfonds (CA); and Colin Joseph Brown, Montreal (CA)
Assigned to Hinge Health, Inc., San Francisco, CA (US)
Appl. No. 17/906,855
Filed by Hinge Health, Inc., San Francisco, CA (US)
PCT Filed Mar. 20, 2020, PCT No. PCT/IB2020/052601
§ 371(c)(1), (2) Date Sep. 20, 2022,
PCT Pub. No. WO2021/186223, PCT Pub. Date Sep. 23, 2021.
Prior Publication US 2023/0154091 A1, May 18, 2023
Int. Cl. G06T 13/40 (2011.01); G06N 3/08 (2023.01); G06T 7/70 (2017.01)
CPC G06T 13/40 (2013.01) [G06N 3/08 (2013.01); G06T 7/70 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, via a communications interface, raw data that includes a first joint position and a second joint position of an input skeleton;
storing the raw data in a memory storage unit;
generating normalized data from the raw data by normalizing the first joint position and the second joint position to conform with a template skeleton;
storing the normalized data in the memory storage unit;
applying, to the normalized data, a neural network with an inverse kinematics engine to infer a joint rotation, wherein the neural network is to use historical data, and wherein training data used to train the neural network includes positional noise; and
adjusting a visual representation of the input skeleton based on the joint rotation.