Neural Networks-Based Approach to Solve Inverse Kinematics Problems for Medical Applications.

ABSTRACT:

Goal

Motion capture is used for recording complex human movements that is increasingly applied in medicine. We describe a novel algorithm of combining a machine learning approach with biomechanics to enable fast and robust analysis of motion capture data to obtain joint angles.

Methods

A multilayer perceptron and a recurrent neural network were compared in their capacity to estimate the joint angles of the human arm. The networks were pre-trained using data from a kinematic model of the human arm. The data comprised movements of three degrees of freedom, such as wrist flexion/extension, wrist ulnar/radial deviation, and hand pronation/supination.

Results

A recurrent neural network model with long short-term memory architecture can solve the inverse kinematics problem

SUBMITTER: Korol AS 

PROVIDER: S-EPMC11883177 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature