View source for 18205472. ESTIMATING GAIT EVENT TIMES & GROUND CONTACT TIME AT WRIST simplified abstract (Apple Inc.)

=ESTIMATING GAIT EVENT TIMES & GROUND CONTACT TIME AT WRIST=


==Organization Name==

[[:Category:Apple Inc.|Apple Inc.]]


[[Category:Apple Inc.]]

==Inventor(s)==

[[:Category:Allison L. Gilmore of Redwood City CA (US)|Allison L. Gilmore of Redwood City CA (US)]][[Category:Allison L. Gilmore of Redwood City CA (US)]]

[[:Category:Adeeti V. Ullal of Emerald Hills CA (US)|Adeeti V. Ullal of Emerald Hills CA (US)]][[Category:Adeeti V. Ullal of Emerald Hills CA (US)]]

[[:Category:Alexander G. Bruno of Cupertino CA (US)|Alexander G. Bruno of Cupertino CA (US)]][[Category:Alexander G. Bruno of Cupertino CA (US)]]

[[:Category:Eugene Song of San Jose CA (US)|Eugene Song of San Jose CA (US)]][[Category:Eugene Song of San Jose CA (US)]]

[[:Category:Gabriel A. Blanco of San Francisco CA (US)|Gabriel A. Blanco of San Francisco CA (US)]][[Category:Gabriel A. Blanco of San Francisco CA (US)]]

[[:Category:James J. Dunne of San Francisco CA (US)|James J. Dunne of San Francisco CA (US)]][[Category:James J. Dunne of San Francisco CA (US)]]

[[:Category:João Antunes of Campbell CA (US)|João Antunes of Campbell CA (US)]][[Category:João Antunes of Campbell CA (US)]]

[[:Category:Karthik Jayaraman Raghuram of Foster City CA (US)|Karthik Jayaraman Raghuram of Foster City CA (US)]][[Category:Karthik Jayaraman Raghuram of Foster City CA (US)]]

[[:Category:Po An Lin of San Jose CA (US)|Po An Lin of San Jose CA (US)]][[Category:Po An Lin of San Jose CA (US)]]

[[:Category:Richard A. Fineman of Campbell CA (US)|Richard A. Fineman of Campbell CA (US)]][[Category:Richard A. Fineman of Campbell CA (US)]]

[[:Category:William R. Powers, Iii of San Francisco CA (US)|William R. Powers, Iii of San Francisco CA (US)]][[Category:William R. Powers, Iii of San Francisco CA (US)]]

[[:Category:Asif Khalak of Belmont CA (US)|Asif Khalak of Belmont CA (US)]][[Category:Asif Khalak of Belmont CA (US)]]

==ESTIMATING GAIT EVENT TIMES & GROUND CONTACT TIME AT WRIST - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18205472 titled 'ESTIMATING GAIT EVENT TIMES & GROUND CONTACT TIME AT WRIST

==Simplified Explanation==

The abstract describes a patent application for estimating gait time events and GCT (Ground Contact Time) using a wrist-worn device. The method involves obtaining sensor data related to acceleration and rotation rate from the device and using a machine learning model to predict gait event times based on this data.

* The patent application focuses on estimating gait time events and GCT using a wrist-worn device.
* The method involves obtaining sensor data related to acceleration and rotation rate from the device.
* A machine learning model is used to predict at least one gait event time based on the sensor data.
* The acceleration and rotation rate serve as input to the machine learning model.

== Potential Applications ==
* Fitness tracking devices that can provide accurate gait analysis and performance metrics.
* Rehabilitation devices that can monitor and analyze gait patterns for patients recovering from injuries or surgeries.
* Sports training devices that can provide real-time feedback on gait performance and help improve technique.

== Problems Solved ==
* Accurately estimating gait time events and GCT using a wrist-worn device.
* Overcoming the limitations of existing methods that may require specialized equipment or be less portable.
* Providing a convenient and accessible solution for monitoring and analyzing gait patterns.

== Benefits ==
* Improved accuracy in estimating gait time events and GCT.
* Increased convenience and portability with a wrist-worn device.
* Potential for real-time feedback and analysis, leading to better gait performance and technique.


==Original Abstract Submitted==

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.



[[Category:A61B5/11]]
[[Category:G16H50/20]]
[[Category:A61B5/00]]