US 12,268,495 B2
Method and system utilizing pattern recognition for detecting atypical movements during physical activity
Ronald Reed Ferber, Calgary (CA); Dylan Robert John Kobsar, Vancouver (CA); Sean Thomas Osis, Cochrane (CA); Christian Arthur Clermont, Calgary (CA); and Lauren Christine Benson, Calgary (CA)
Assigned to UTI Limited Partnership, Alberta (CA)
Appl. No. 16/764,648
Filed by UTI Limited Partnership, Calgary (CA)
PCT Filed Nov. 14, 2018, PCT No. PCT/CA2018/051442
§ 371(c)(1), (2) Date May 15, 2020,
PCT Pub. No. WO2019/095055, PCT Pub. Date May 23, 2019.
Claims priority of provisional application 62/586,565, filed on Nov. 15, 2017.
Prior Publication US 2021/0369143 A1, Dec. 2, 2021
Int. Cl. A61B 5/11 (2006.01); A61B 5/00 (2006.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G16H 20/30 (2018.01)
CPC A61B 5/1118 (2013.01) [A61B 5/112 (2013.01); A61B 5/6801 (2013.01); G06N 20/00 (2019.01); G16H 20/30 (2018.01); A61B 2560/0242 (2013.01); A61B 2562/04 (2013.01)] 17 Claims
OG exemplary drawing
 
9. A method comprising:
receiving, by at least one computer processor, individualized movement profile information for a user from a database, wherein the individualized movement profile information defines a subspace in which movements of the user are considered typical for the user, and wherein the individualized movement profile information corresponds to external condition information for providing context to the individualized movement profile of the user;
receiving, by at least one computer processor, new movement information of the user relating to a physical activity from one or more wearable motion sensors;
receiving, by at least one computer processor, new external condition information associated with the new movement information;
determining, by at least one computer processor, if the movements of the user during the physical activity are typical for the user by determining if the movements are located within the subspace;
generating, by at least one computer processor, in response to determining that the movements of the user during the physical activity are typical for the user, a first output indication signal;
identifying, by at least one computer processor, in response to determining that the movements of the user during the physical activity are not typical for the user, a subgroup from among a plurality of subgroups that most closely corresponds to the new movement information, wherein each subgroup consist of movement information of users all sharing on one or more predetermined characteristics, wherein each subgroup defines a subspace associated with the one or more predetermined characteristics of the users of the subgroup such that such that the movements of the users of the subgroup according to the movement information are located within the subspace of the subgroup, wherein the subspace of the subgroup comprises a multivariate threshold boundary such that the movements of the users of the subgroup according to the movement information are located within the multivariate threshold boundary of the subgroup, and wherein at least one of the determining if the movements of the user during the physical activity are typical for the user, and the identifying a subgroup from among the plurality of subgroups that most closely corresponds to the new movement information is based on the received new external condition information;
determining, by at least one computer processor, if the identified subgroup is the same or different as a predefined subgroup associated with the user;
generating, by at least one computer processor, in response to determining that the identified subgroup is the same as the predefined subgroup, a second output indication signal;
generating, by at least one computer processor, in response to determining that the identified subgroup is different than the predefined subgroup, a third output indication signal.