Systems and methods for approximating musculoskeletal dynamics

Patent metadata

Field	Value
Publication number	US-11744720-B2
Application number	US-201816643163-A
Country	US
Kind code	B2
Filing date	Sep 18, 2018
Priority date	Sep 18, 2017
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

An approximation method and system are provided for more quickly controlling a prosthetic or other device by reducing computational processing time in a muscle model that can be used to control the prosthetic. For a given muscle, the approximation method can quickly compute polynomial structures for a muscle length and for each associated moment arms, which may be used to generate a torque for a joint position of a physics model. The physics model, in turn, produces a next joint position and velocity data for driving a prosthetic. The approximation method expands the polynomial structures as long as expansion is possible and sufficiently beneficial. The computations can be performed quickly by expanding the polynomial structures in a way that constrains the muscle length polynomial to the moment arm polynomial structures, and vice versa.

First claim

The invention claimed is:

1. An approximation method performed in a processor for generating a model, the approximation method comprising:
receiving an input dataset associated with at least a first muscle length and at least a first moment arm associated with the first muscle length;
using the input dataset to generate at least a first muscle length polynomial and at least a first moment arm polynomial associated with the first muscle length and said at least a first moment arm, respectively;
expanding the first muscle length polynomial by at least one additional term while improving an accuracy of the first muscle length polynomial based on information related to said at least a first moment arm;
expanding said at least a first moment arm polynomial by at least one additional term while improving an accuracy of said at least a first moment arm polynomial based on information related to the first muscle length;
integrating the expanded first moment polynomial to produce one or more integrals;
joining said one or more integrals with the expanded first muscle length polynomial to obtain a constrained muscle length polynomial;
differentiating the constrained muscle length polynomial to obtain one or more constrained moment arm polynomials; and
approximating dynamics of a device based at least in part on the expanded first muscle length polynomial, the expanded first moment arm polynomial, the constrained muscle length polynomial, and the one or more constrained moment arm polynomials.

2. The approximation method of claim 1 , wherein expanding the first muscle length polynomial and expanding the first moment arm polynomial further comprises:
generating at least a first list of potential candidates for expanding the first muscle length polynomial by at least one additional term and generating a second list for expanding the first moment arm polynomial by at least one additional term;
selecting a first candidate from the first list for expanding the first muscle length polynomial and selecting a second candidate from the second list for expanding the first moment arm polynomial; and
expanding the first muscle length polynomial by the first candidate and expanding the first moment arm polynomial by the second candidate.

3. The approximation method of claim 2 , wherein selecting the first candidate from the first list comprises:
analyzing all of the potential candidates on the first list to determine which of the potential candidates on the first list results in a greatest improvement in fitting the expanded muscle length polynomial to the input dataset.

4. The approximation method of claim 3 , wherein selecting the second candidate from the second list comprises:
analyzing all of the potential candidates on the second list to determine which of the potential candidates on the second list results in a greatest improvement in fitting the expanded first moment arm polynomial to the input dataset.

5. An approximation method performed in a processor for generating a model, the approximation method comprising:
(1) receiving an input dataset associated with at least a first muscle length and at least a first moment arm associated with the first muscle length;
(2) using the input dataset to generate at least a first muscle length polynomial and at least a first moment arm polynomial associated with the first muscle length and said at least a first moment arm, respectively;
(3) generating expanded polynomials comprising an expanded first muscle length polynomial and an expanded first moment arm polynomial by:
generating at least a first list of potential candidates for expanding the first muscle length polynomial by at least one additional term and generating a second list for expanding the first moment arm polynomial by at least one additional term;
selecting a first candidate from the first list for expanding the first muscle length polynomial and selecting a second candidate from the second list for expanding the first moment arm polynomial;
expanding the first muscle length polynomial by the first candidate and expanding the first moment arm polynomial by the second candidate;
integrating the expanded first moment arm polynomial to produce one or more integrals;
joining said one or more integrals with the expanded first muscle length polynomial to obtain a constrained muscle length polynomial; and
differentiating the constrained muscle length polynomial to obtain one or more constrained moment arm polynomials;

(4) determining whether or not the expanded polynomials are further expandable and whether or not further expansion will be beneficial to fitting the expanded polynomials to the input dataset based on the constrained muscle length polynomial and said one or more constrained moment arm polynomials; and
(5) if a determination is made that the expanded polynomials are not further expandable or that further expansion will not be beneficial to fitting the expanded polynomials to the input dataset, approximating dynamics of a device based on the expanded first muscle length polynomial and the expanded first moment arm polynomial.

6. The approximation method of claim 5 , the method further comprising:
if a determination is made at step (4) that the expanded polynomials are further expandable and that further expansion will be beneficial to fitting the expanded polynomials to the input dataset, returning to step (3) and reiterating steps (3) through (5).

7. An approximation method performed in a processor for generating a model, the approximation method comprising:
(1) receiving an input dataset associated with at least a first muscle length and at least a first moment arm associated with the first muscle length;
(2) using the input dataset to generate at least a first muscle length polynomial and at least a first moment arm polynomial associated with the first muscle length and said at least a first moment arm, respectively;
(3) generating expanded polynomials comprising an expanded first muscle length polynomial and an expanded first moment arm polynomial by:
generating at least a first list of potential candidates for expanding the first muscle length polynomial by at least one additional term and generating a second list for expanding the first moment arm polynomial by at least one additional term, wherein an Akaike information criterion (AIC) is calculated for the input dataset;
selecting a first candidate from the first list for expanding the first muscle length polynomial and selecting a second candidate from the second list for expanding the first moment arm polynomial, wherein the AIC is used in analysis that determines which of the potential candidates on the first and second lists result in the greatest improvement in fitting the expanded muscle length polynomial and the expanded first moment arm polynomial, respectively, to the input dataset; and
expanding the first muscle length polynomial by the first candidate and expanding the first moment arm polynomial by the second candidate;

(4) determining whether or not the expanded polynomials are further expandable and whether or not further expansion will be beneficial to fitting the expanded polynomials to the input dataset; and
(5) if a determination is made that the expanded polynomials are not further expandable or that further expansion will not be beneficial to fitting the expanded polynomials to the input dataset, approximating dynamics of a device based on the expanded first muscle length polynomial and the expanded first moment arm polynomial.

8. An approximation method performed on a processor for generating a model, the approximation method comprising:
(1) receiving an input dataset associated with at least a first muscle length and at least a first moment arm as

Assignees

• Univ West Virginia

Inventors

• Sobinov Anton
• Yakovenko Sergiy
• Gritsenko Valeriya
• Hardesty Russell
• Boots Matthew

Classifications

• A61F2/72 Primary

  Bioelectric control, e.g. myoelectric · CPC title

• A61B5/1107

  Measuring contraction of parts of the body, e.g. organ or muscle · CPC title

• A61B5/4519

  Muscles (measuring muscular strength A61B5/224; using electromyography [EMG] A61B5/389) · CPC title

• A61F2/582

  Elbow joints · CPC title

• A61F2/70

  electrical · CPC title