公开(公告)号：US20230389821A1

公开(公告)日：2023-12-07

申请号：US18205476

申请日：2023-06-02

Applicant: Apple Inc.

Inventor： Richard A. Fineman ,  Adeeti V. Ullal ,  Allison L. Gilmore ,  Gabriel A. Blanco ,  Karthik Jayaraman Raghuram ,  Mark P. Sena ,  Maryam Etezadi-Amoli ,  James J. Dunne ,  Po An Lin

IPC: A61B5/11 ,  A61B5/00

CPC classification number: A61B5/1114 ,  A61B5/681 ,  A61B5/7264 ,  A61B2562/0219

Abstract: Enclosed are embodiments for estimating vertical oscillation (VO) at the wrist. In some embodiments, a method comprises: obtaining, with a wearable device worn on a wrist of a user, sensor data indicative of the user's acceleration and rotation rate; estimating centripetal acceleration based on the user's acceleration and rotation rate; calculating a modified user's acceleration by subtracting the estimated centripetal acceleration from the user's acceleration; estimating center of mass (CoM) acceleration by decoupling an arm swing component of the user's acceleration from the modified user's acceleration; and computing vertical oscillation of the user's CoM using a machine learning model with at least the CoM acceleration as input to the machine learning model, or by integrating vertical acceleration derived from the CoM acceleration and a gravity vector.

公开(公告)号：US20230389824A1

公开(公告)日：2023-12-07

申请号：US18205472

申请日：2023-06-02

Applicant: Apple Inc.

Inventor： Allison L. Gilmore ,  Adeeti V. Ullal ,  Alexander G. Bruno ,  Eugene Song ,  Gabriel A. Blanco ,  James J. Dunne ,  João Antunes ,  Karthik Jayaraman Raghuram ,  Po An Lin ,  Richard A. Fineman ,  William R. Powers, III ,  Asif Khalak

IPC: A61B5/11 ,  G16H50/20 ,  A61B5/00

CPC classification number: A61B5/112 ,  G16H50/20 ,  A61B5/681 ,  A61B5/7267

Abstract: Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.