Inseer Inc.

Dmitriy Shin

Jean E Robillard

Mitchell Messmore

John Rachid

Alec Diaz-Arias

Aiec Diaz-Arias

An apparatus for 3D human pose estimation using dynamic multi-headed convolutional attention mechanism is presented. The apparatus contains two dynamic multi-headed convolutional attention mechanism with spatial attention and another with temporal attention that leverages the spatial attention mechanism to extract frame-wise inter-joint dependencies by analyzing sections of limbs that are related. The temporal attention mechanism extracts global inter-frame relationships by analyzing correlations between the temporal profile of joints. The temporal profile mechanism leads to a more diverse temporal attention map while achieving substantial parameter reduction.

Methods and apparatus for human pose estimation from images using dynamic multi-headed convolutional attention

An apparatus for calculating torque and force about body joints to predict muscle fatigue includes a processor configured to receive image frames depicting a subject. The processor is configured to execute at least one machine learning model using the image frames as an input, to generate a 2D representation of the subject, a subject mass value for the subject based on the 2D representation, and a 3D representation of the subject based on the 2D representation, where the 3D representation includes a temporal joints profile. The processor is further configured to compute each torque value for each joint of the subject from the 3D representation, based on the subject mass value. The processor is further configured to generate a muscle fatigue prediction for each joint of the subject, based on a set of torque values and a torque threshold.

Growing community of inventors

Columbia, MO, United States of America

Dmitriy Shin