Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
Neural Networks-Based Approach to Solve Inverse Kinematics Problems for Medical Applications.
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Korol Anna S, Rodzin Taras, Zabava Kateryna, Gritsenko Valeriya
Аннотация
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.
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.