Бібліографічний опис:
Zabava, Kateryna. Real-time inverse kinematics and inverse dynamics from motion capture / Kateryna Zabava; Supervisor: Dr. Valeriya Gritsenko; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 45 p.: ill.
Короткий опис (реферат):
This work applies machine learning to solving inverse dynamics and inverse kinematics
tasks from the motion capture data. This approach may simplify the calculation
process and help do scientific simulations as part of a physics engine that
describes the neural control of human motion and decodes movement intent in individuals
with neural damage. The existing algorithm has to be modified for every
experiment and takes a significant amount of time to execute. It is also sensitive to
noise and missing data, and it is not a real-time calculation. We propose a solution
of inverse kinematics tasks with neural networks. Here we report accuracy results
both on clean data and noisy data. We also apply a similar approach for the inverse
dynamics task. The approach shows high accuracy on clean data, but this accuracy
decreases if applied to the noisy data.
