US 11,673,024 B2
Method and system for human motion analysis and instruction
Alex B. Omid-Zohoor, Scottsdale, AZ (US); Brian Vermilyea, Scottsdale, AZ (US); Erik Herter, Scottsdale, AZ (US); Tony Morgan, Scottsdale, AZ (US); Steve Diamond, Scottsdale, AZ (US); Michael Chu, Scottsdale, AZ (US); and Eran Leshem, Scottsdale, AZ (US)
Assigned to PG TECH, LLC, Scottsdale, AZ (US)
Filed by PG Tech, LLC, Scottsdale, AZ (US)
Filed on Jan. 21, 2019, as Appl. No. 16/253,155.
Claims priority of provisional application 62/620,325, filed on Jan. 22, 2018.
Claims priority of provisional application 62/620,296, filed on Jan. 22, 2018.
Prior Publication US 2019/0224528 A1, Jul. 25, 2019
Int. Cl. A63B 24/00 (2006.01); A61B 5/11 (2006.01); A61B 5/00 (2006.01); G16H 50/20 (2018.01); G16H 20/30 (2018.01); G16H 40/67 (2018.01); A63B 102/32 (2015.01)
CPC A63B 24/0075 (2013.01) [A61B 5/0022 (2013.01); A61B 5/0024 (2013.01); A61B 5/1114 (2013.01); A61B 5/1123 (2013.01); A61B 5/6805 (2013.01); A61B 5/6806 (2013.01); A61B 5/6895 (2013.01); A61B 5/742 (2013.01); A63B 24/0003 (2013.01); G16H 20/30 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); A61B 2562/0219 (2013.01); A63B 2102/32 (2015.10)] 9 Claims
OG exemplary drawing
 
1. A method for processing and training a dynamic body motion, comprising:
receiving, by a central processing unit (CPU), sensor data for a dynamic body motion for an exercise from one or more sensors worn by a user that are configured to communicate the sensor data;
analyzing, by the CPU, the received sensor data to form motion data related to one or more components of the dynamic body motion;
comparing, by the CPU, the motion data with stored motion parameters for a motion template T stored in a motion database;
computing, by the CPU, a similarity score between the motion data and the stored motion parameters for the motion template T;
generating, by the CPU, in real time a first biofeedback signal when the similarity score is outside a predetermined acceptable range, and in real time a second biofeedback signal that is different than the first biofeedback signal when the similarity score is within the predetermined acceptable range; and
automatically prescribing, by the CPU, an exercise regime stored in the motion database based upon the computed similarity score for use in a biofeedback training exercise,
wherein the similarity score is computed by:
creating the motion template T having an M×N matrix, wherein each of the M rows represents a motion parameter time series of length N,
creating a motion template S having an M×K matrix from the received sensor data consisting of K samples, where K≥N, such that each of the M rows represents the same motion parameters included in the motion template T,
aligning the motion template S with the motion template T using cross-correlation and truncate non-overlapping columns as follows:
i. selecting a motion parameter row to use for alignment,
ii. calculating a lag τ between Ti,* and

OG Complex Work Unit Math
iii. If 0≤τ≤K−N, then truncate the first τ columns and last (K−N−τ) columns of S to yield M×N matrix

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and if τ<0 or τ>K−N, then stop and raise an error indicating that the received sensor data does not contain data matching the entire motion template T, and
computing the similarity score as a weighted sum of normalized root mean square error (NRMSE) values between corresponding rows of Ŝ and T wherein each value wi is a scalar weight applied to the NRMSE for row i

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wherein Ti is a time series of a single motion parameter from the motion template T for a row i of the M×N matrix, Si is a time series of a single motion parameter from the motion template S for a row i of the M×K matrix, and n refers to an index of each time series Ti and Si.