US 2025/0157072 A1 · Techniques For Real-Time Estimation And Visualization Of Muscle Activations

Abstract

Techniques are disclosed for estimating the activity or activation levels of muscles of a subject. The estimation can be done at real-time or near real-time rates in response to live performance of body motions of the subject. The estimates are computed by a machine learning model which is preferably a long short-term memory (LSTM) recurrent neural network (RNN). The LSTM RNN computes the estimates as output based on 3D pose estimates of the subject as input. The muscle activation estimates can be used to visually express which muscles are active on a display interface such as that of an augmented reality (AR) system. They can also be used to actuate or control other devices such as lighting in smart clothing or prosthetic devices worn by the user. They can also be used to actuate external robotic mechanisms.

Claims20

Dependent claims

A system comprising:

(a) a camera for capturing a motion video of a subject, said subject comprising a body segment and a muscle;
(b) a pose estimator for analyzing said motion video and for generating a time-series of three-dimensional (3D) pose estimates of said body segment, wherein said pose estimates are relative to a parent body segment;
and
(c) a trained recurrent neural network (RNN);
wherein said trained RNN receives said time-series and generates an estimate of an activation of said muscle.

2 more elements

Dependencies are inferred from the claim text.

Techniques For Real-Time Estimation And Visualization Of Muscle Activations

Life of this patent

Techniques For Real-Time Estimation And Visualization Of Muscle Activations

Life of this patent

Abstract

Claims20