US 12,675,901 B2
Pose-based identification of weakness
Vishwajith Ramesh, San Diego, CA (US); Nadir Weibel, San Diego, CA (US); Gert Cauwenberghs, San Diego, CA (US); Kunal Agrawal, San Diego, CA (US); and Brett C. Meyer, San Diego, CA (US)
Assigned to The Regents of the University of California, Oakland, CA (US)
Appl. No. 18/547,478
Filed by The Regents of the University of California, Oakland, CA (US)
PCT Filed Feb. 25, 2022, PCT No. PCT/US2022/017944
§ 371(c)(1), (2) Date Aug. 22, 2023,
PCT Pub. No. WO2022/183017, PCT Pub. Date Sep. 1, 2022.
Claims priority of provisional application 63/153,902, filed on Feb. 25, 2021.
Prior Publication US 2024/0185453 A1, Jun. 6, 2024
Int. Cl. G06K 9/00 (2022.01); G06T 7/73 (2017.01); G06V 10/44 (2022.01); G06V 20/40 (2022.01)
CPC G06T 7/73 (2017.01) [G06V 10/44 (2022.01); G06V 20/44 (2022.01); G06T 2207/20044 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving, using at least one processor, one or more video recordings of one or more subjects;
extracting, using the at least one processor, one or more features from each of the one or more video recordings, the one or more features corresponding to one or more bodily joints of the one or more subjects;
training, using the at least one processor, one or more models based on the extracted features to identify a weakness in the one or more bodily joints; and
applying, using the at least one processor, the trained model to a first video recording in the one or more video recordings of a first subject in the one or more subjects, and determining, using the applied model, whether a weakness is present in one or more bodily joints of the first subject wherein the applying includes determining one or more spatial coordinates corresponding to a position of one or more bodily joints of the first subject;
generating, using the at least one processor, a covariance of the determined one or more spatial coordinates;
executing, using the at least one processor, one or more machine learning processes and/or one or more deep learning processes, using the generated covariance; and
determining, using the at least one processor, whether a weakness is present in one or more bodily joints of the first subject based on the executed one or more machine learning processes and/or the executed one or more deep learning processes.