Abstract—Numerous investigations have explored automated control mechanisms for artificial hands; nonetheless, the performance of individual robotic fingers remains suboptimal. To enhance motion precision, this study proposes a model of hand movement control utilizing Artificial Intelligence (AI). Selecting suitable sensing components and applying specialized computational algorithms are key factors in optimizing the prosthetic control framework. A control architecture based on machine learning was developed, enabling the hand prosthesis prototype to perform movements automatically according to the classifications generated by the trained model. Experimental results demonstrate that increasing the neuron count in the learning model enhances predictive accuracy while minimizing loss values. A comparison between Artificial Neural Network (ANN) and Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architectures revealed that the ANN configuration with a 32-neuron hidden layer provided the best results, achieving a loss below 0.1 and accuracy above 90%. Therefore, this ANN model was chosen as the main control algorithm for the prosthetic system. When implemented in the master controller, the model produced prediction accuracies exceeding 90% across all output classes, successfully activating the prosthetic hand according to ten predefined motion labels.

Keywords—intelligent control, prosthetic hand, motion coordination, artificial neural network, sensor feedback

Cite: I Wayan Widhiada, I Made Widiyarta, I Made Esa Dharmayasa Putra, I Gede Putu Agus Suryawan, Dewa Ngakan Ketut Putra Negara, and Tjokorde Gede Tirta Nindhia, "System Classification of Hand Movements in Hand Prosthesis Prototype Based on Model Machine Learning Control," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 123-134, 2026. doi: 10.18178/ijmerr.15.2.123-134

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).