Бібліографічний опис:
Manukian, Mykhailo. Real-time simulation of arm and hand dynamics using ANN / Mykhailo Manukian; Supervisor: Dr. Sergiy Yakovenko; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 53 p.: ill.
Короткий опис (реферат):
The physics of body dynamics is a complex problem solved by the nervous system
in real-time during the planning and execution of movements. The human arm
and hand have complex mechanics involving hundreds of muscles that actuate over
30 degrees of freedom (DOF). To date, the problems of this complexity remain unsolved
in engineering; yet, the nervous system computes control signals in a robust,
accurate, and time-efficient manner. Neuroprosthetics require similar computations
for the decoding of intent and encoding of sensory feedback. The trade-off of required
computational accuracy and latency is hard to resolve with classical physics;
thus, this research aims to develop "good-enough" approximations of these computations
using machine learning methods, such as artificial neural networks (ANN).
The kinematic and kinetic temporal computations that rely on the diverse number
of terms within the equations of motion are consistent with the recurrent neural
network (RNN) architectures. This study will test the general hypothesis that the
inverse dynamics of arm and hand can be captured with RNN formulation and explore
the utility of different architectures: i) simple Recurrent ANN, ii) Gated Recurrent
Unit (GRU) ANN, and iii) Long Short-Term Memory (LSTM) ANN. The inverse
problem is the mapping from joint kinematics (position, velocity, acceleration)
to joint kinetics (torque). The training and testing datasets were derived from the
physical model of arm and hand performing point-to-point movements between realistic
postures arranged in a grid within the physiological range of motion. Lastly,
we assessed the execution latency of the machine learning solutions in the context
of real-time requirements for prosthetic applications.
