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

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 for three rotational degrees of freedom with the least error; it performed faster than real time. The predictions were robust against noise.

CONCLUSIONS

This shows that it is feasible to rely on pre-trained neural networks for real-time calculation of joint angles.