EMG Controlled Bionic Robotic Arm using Artificial Intelligence and Machine Learning

Sree, K P N V Satya; Bikku, Thulasi; Mounika, S; Ravinder, N.; Kumar, Mandhir; Prasad, Chitturi

doi:10.1109/i-smac52330.2021.9640623

Cited by 12 publications

(2 citation statements)

References 13 publications

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“…In some recent studies in the literature, it is seen that only PID controllers [28][29][30][31] control robot arms. Again, in some recent studies in the literature, it is seen that the data obtained from EMG muscle signals only work with fuzzy controllers or machine learning algorithms in this direction [32][33][34][35][36][37]. Many studies have compared EMG signal data with different control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Ersin,

Yaz

2024

Journal of Sensors

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In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.

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Section: Introductionmentioning

confidence: 99%

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Ersin,

Yaz

2024

Journal of Sensors

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“…In this line of research, it is possible to cite works in which the researchers developed some type of prototype of upper limb prosthesis commanded from biopotential signals (du Plessis et al, 2021; Herbst et al, 2021; Lins et al, 2019; Nadjib et al, 2021; Sree et al, 2021). In these, the researchers used some Machine Learning algorithm to classify the signals.…”

Section: Introductionmentioning

confidence: 99%

Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command

Barbosa

Neto

et al. 2022

Expert Systems

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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.

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IoT‐Based Renewable Energy Management Systems in Apartment

Bikku,

Sivakumar,

Chinthamani

et al. 2024

Sustainable Smart Homes and Buildings With Internet of Things

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EMG Controlled Bionic Robotic Arm using Artificial Intelligence and Machine Learning

Cited by 12 publications

References 13 publications

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command

IoT‐Based Renewable Energy Management Systems in Apartment

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