Academic Journal
Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command.
| Τίτλος: | Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command. |
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| Συγγραφείς: | Xu, Yongzhao, Barbosa, Paulo C. S., da Cunha Neto, Joel S., Zhang, Lijuan, Shanmuganathan, Vimal, de Albuquerque, Victor Hugo C., Pasupathi, Subbulakshmi |
| Πηγή: | Expert Systems; Jun2023, Vol. 40 Issue 5, p1-15, 15p |
| Θεματικοί όροι: | Pattern recognition systems, Artificial intelligence, Prosthetics, Graphical user interfaces, Robotics, Standard of living |
| Περίληψη: | Prostheses play an important role in the rehabilitation of people who have suffered some type of amputation. However, due to its high‐cost and high complexity in performing movements of everyday tasks, users of these prostheses may encounter many difficulties. Therefore, this work proposes the development of a future artificial intelligence technology based on a low‐cost functional prosthesis prototype (manufactured in a 3D printer). In the present work, we describe an intelligent system that uses an artificial neural network to recognize patterns in muscle biopotential signals in order to control a prosthesis prototype in real time. Such a system is divided into three parts: the first that performs a human–machine integration through a graphical user interface; the second that performs the signal acquisition; the third that performs the training and generalization steps of the artificial neural network. The developed interface runs on a web application that has a database hosted in the cloud and in it the system user can: Acquisition of electromyography signals; Training phase of the artificial neural network; Sends the matrix of weights of the trained network to the microcontroller; Activates in the microcontroller, the state of action of the commands from the identified gestures. To compose the results of the present work, a search was initially carried out for the ideal parameters of the artificial neural network through signals obtained from 20 volunteers. In this step, it was possible to identify the topology that best classifies the signals of each gesture, as well as the investigation of the number of neurons in the hidden layer that causes a low generalization power due to overfitting. At the end of the project, it was possible to validate the use of the system with 15 new volunteers, and it was observed that in most cases, the performance of the commands in the prosthesis prototype were performed correctly. In addition, a project cost analysis was carried out, and it was possible to verify that the prototype developed is viable and has an affordable cost in relation to the Brazilian cost of living standards. In this way, the objective of the present work is in the development of a low cost artificial intelligence technology. Such a system is equipped with an algorithm based on neural networks that can deal with different muscle biopotential signals, in order to command a robotic prosthesis. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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