Convolutional Neural Network (CNN) implementation on bionic hand in comparison with Recurrent Neural Network (RNN)

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Convolutional Neural Network (CNN) implementation on bionic hand in comparison with Recurrent Neural Network (RNN)

Setyawan, Luke Indracahya - Personal Name; Budiarto, Eka - Personal Name;

The main objective of this thesis is to improve the implementation of gesture classifier on Bionic Hand, which uses the Electromyography (EMG) signal from the Myo Armband device. The improvement is mainly on the accuracy and the additional number of gestures to be added. It utilized the Convolutional Neural Network (CNN) machine learning as the main gesture classifier, and then was compared with Recurrent Neural Network (RNN) machine learning. This thesis also includes the development and modification of the program script, along with the analysis of each results. At the end, the CNN was able to achieve an accuracy of 90.32%, while the RNN gesture classifier could reach higher accuracy at 96.32%. Besides that, the pointing gesture and rock gesture are successfully implemented and classified by both methods.

Availability

B02966 (Rack Thesis) Available but not for loan - Missing

Detail Information

Series Title: -
Call Number: 2966
Publisher: : Swiss German University., 2019
Collation: -
Language: English
ISBN/ISSN: -
Classification: NONE
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): Machine learning
MT
Electromyography
Convolutional Neural Network
Bionic Hand
Recurrent Neural Network
Gesture Classification
Specific Detail Info: -
Statement of Responsibility: -

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