Motion monitoring methods and systems

Patent metadata

Field	Value
Publication number	US-12566501-B2
Application number	US-202318183923-A
Country	US
Kind code	B2
Filing date	Mar 14, 2023
Priority date	Mar 19, 2021
Publication date	Mar 3, 2026
Grant date	Mar 3, 2026

Abstract

A motion monitoring method (500) is provided, which includes: obtaining a movement signal of a user during motion, wherein the movement signal includes at least an electromyographic signal or an attitude signal (510); and monitoring a movement of the user during motion based at least on feature information corresponding to the electromyographic signal or the feature information corresponding to the attitude signal (520).

First claim

What is claimed is:

1 . A motion monitoring method, comprising:
obtaining a movement signal of a user during motion, the movement signal comprising at least an electromyographic signal and an attitude signal; and segmenting, based on feature information corresponding to the electromyographic signal and feature information corresponding to the attitude signal, the movement signal, wherein the feature information corresponding to the electromyographic signal includes at least frequency information or amplitude information, and the feature information corresponding to the attitude signal includes at least one of an angular velocity direction, an angular velocity value, an acceleration of an angular velocity, an angle, displacement information, and stress; determining, based on at least one segment of the movement signal through a movement recognition model, movement-related information, wherein the movement recognition model is a trained machine learning model, the movement-related information includes movement quality and the movement quality includes a standard movement or a wrong movement; and in responding to determining that the movement quality is the wrong movement, controlling an electromyography sensor to generate an electrical stimulation signal to prompt the user to make a motion adjustment.

2 . The motion monitoring method of claim 1 , wherein the segmenting, based on the feature information corresponding to the electromyographic signal and the feature information corresponding to the attitude signal, the movement signal includes:
segmenting the attitude signal the feature information corresponding to the attitude signal according to operations including:
determining, based on a time domain window of the attitude signal, at least one target feature point from the time domain window according to a preset condition, wherein the at least one target feature point includes one of a movement start point, a movement middle point, and a movement end point; and
segmenting, based on the at least one target feature point, the attitude signal.

3 . The motion monitoring method of claim 2 , wherein the preset condition includes one or more of a change in the angular velocity direction corresponding to the attitude signal; the angular velocity corresponding to the attitude signal being greater than or equal to an angular velocity threshold; a changed value of the angular velocity value corresponding to the attitude signal being an extreme value; and the angle corresponding to the attitude signal reaching an angular threshold;
wherein the preset condition further includes the acceleration of the angular velocity corresponding to the attitude signal being continuously greater than or equal to an acceleration threshold of the angular velocity for a first specific time range.

4 . The motion monitoring method of claim 1 , wherein segmenting, based on the feature information corresponding to the electromyographic signal and the feature information corresponding to the attitude signal, the movement signal includes:
segmenting the electromyographic signal based on the feature information corresponding to the electromyographic signal according to operations including: pre-processing the electromyographic signal in a frequency domain or a time domain; obtaining, based on the pre-processed electromyographic signal, the feature information corresponding to the electromyographic signal; and segmenting the electromyographic signal based on the feature information corresponding to the electromyographic signal.

5 . The motion monitoring method of claim 4 , wherein the pre-processing the electromyographic signal in a frequency domain or a time domain includes:
filtering the electromyographic signal to select components of the electromyographic signal in a specific frequency range in the frequency domain.

6 . The motion monitoring method of claim 4 , wherein the pre-processing the electromyographic signal in a frequency domain or a time domain includes:
performing a signal correction processing on the electromyographic signal in the time domain.

7 . The motion monitoring method of claim 6 , wherein the performing a signal correction processing on the electromyographic signal in the time domain includes:
determining a singularity in the electromyographic signal, wherein the singularity corresponds to an abrupt signal of the electromyographic signal; and performing the signal correction processing on the singularity in the electromyographic signal; wherein the performing the signal correction processing on the singularity in the electromyographic signal includes: removing the singularity, or correcting the singularity according to a signal around the singularity.

8 . The motion monitoring method of claim 7 , wherein the singularity includes a burr signal, the determining the singularity in the electromyographic signal includes:
selecting, based on the time domain window of the electromyographic signal, different time windows from the time domain window of the electromyographic signal, wherein the different time windows respectively cover different time ranges; and determining, based on the feature information corresponding to the electromyographic signal in the different time windows, the burr signal.

9 . The motion monitoring method of claim 8 , wherein the determining, based on the feature information corresponding to the electromyographic signal in the different time windows, the burr signal, includes:
determining first amplitude information corresponding to the electromyographic signal within a first time window and second amplitude information corresponding to the electromyographic signal within a second time window, wherein the first time window and the second time window are two adjacent time windows; determining whether a ratio of the second amplitude information to the first amplitude information is greater than a threshold: in response to determining that the ratio of the second amplitude information to the first amplitude information is greater than the threshold, performing a signal correction processing on the electromyographic signal within the second time window; in response to determining that the ratio of the second amplitude information to the first amplitude information is not greater than the threshold, retaining the electromyographic signal within the second time window.

10 . The motion monitoring method of claim 1 , further comprising determining, based on the attitude signal, the feature information corresponding to the attitude signal, wherein the attitude signal comprises coordinate information in at least one original coordinate system; and
determining, based on the attitude signal, the feature information corresponding to the attitude signal comprises: obtaining a target coordinate system and a conversion relationship between the target coordinate system and the at least one original coordinate system; converting, based on the conversion relationship, the coordinate information in the at least one original coordinate system to coordinate information in the target coordinate system; and determining, based on the coordinate information in the target coordinate system, the feature information corresponding to the attitude signal; wherein the target coordinate system changes as an orientation of the user changes.

11 . The motion monitoring method of claim 10 , wherein the attitude signal includes coordinate information generated by at least two sensors, the at least two sensors are located at different motion parts of the user and correspond to different original coordinate systems, the determining, based on the attitude signal, the feature inf

Assignees

• Shenzhen Shokz Co Ltd

Inventors

• Su Lei
• Zhou Xin
• Li Meiqi
• Liao Fengyun

Classifications

• A61B5/7225

  Details of analogue processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation (input circuits for detecting, measuring, or recording bioelectric or biomagnetic signals A61B5/30; specific diagnostic methods using bioelectric or biomagnetic signals A61B5/316) · CPC title

• A61B5/1116

  Determining posture transitions · CPC title

• A61B5/397

  Analysis of electromyograms · CPC title

• G06F18/2415

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title