EMG controlled bionic robotic arm using artificial intelligence and machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/14728 |
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10361-14728 |
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10361-147282022-01-26T10:08:18Z EMG controlled bionic robotic arm using artificial intelligence and machine learning Rupom, Farhan Fuad Johan, Gazi Musa Al Jannat, Shafaitul Tamanna, Farjana Ferdousi Islam, Md. Motaharul Department of Computer Science and Engineering, Brac University Electromyography Hand gestures K-nearest Neighbor (KNN) Support Vector Machine (SVM) EMG MyoWare Muscle Sensor Autodesk 3D Max software Prosthetic Biomaterials. Artificial intelligence. Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-47). Electromyography is a unique approach for recording and analyzing the electrical activity generated by muscles, and a Myo-electric controlled prosthetic limb is an outwardly controlled artificial limb which is controlled by the electrical signals instinctively produced by the muscle system itself. Artificial Intelligence and Machine learning is very powerful in every technological field along with biomedical field. The purpose of this work is to utilize the power of Machine learning and Deep learning for predicting and recognizing hand gestures for prosthetic hand from collecting data of muscle activities. Although this technology already exists in the technological world but those are very costly and not available in developing countries. So, designing a cost effective prosthetic hand with the maximize accuracy is the major focus and objective of this work. We have also used a data set recorded by MyoWare Muscle Sensor which represents uninterrupted readings from 8 sensors. We have used Deep learning with the data set for predicting the following gestures which are handopen, hand-close, spherical-grip, and fine-pinch. Then we used some algorithms of Machine Learning which are K-nearest Neighbor (KNN), Support Vector Machine (SVM), and also the combination of KNN and SVM both for feature classification on data recorded with the 8-electrode surface EMG (sEMG) MyoWare Muscle Sensor. Using the combination of SVM and KNN We have accomplished a real time test accuracy of 96.33 percent at classifying the four gestures of our prosthetic hand. This paper also represents 3D modeling of the robotic hand and its control system using Autodesk 3D’s Max software, EMG MyoWare Muscle Sensor, Machine Learning and Deep Learning. Farhan Fuad Rupom Gazi Musa Al Johan Shafaitul Jannat Farjana Ferdousi Tamanna B. Computer Science 2021-07-03T15:24:27Z 2021-07-03T15:24:27Z 2020 2020-04 Thesis ID 16301122 ID 16101050 ID 16301050 ID 16301173 http://hdl.handle.net/10361/14728 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 47 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Electromyography Hand gestures K-nearest Neighbor (KNN) Support Vector Machine (SVM) EMG MyoWare Muscle Sensor Autodesk 3D Max software Prosthetic Biomaterials. Artificial intelligence. Machine learning. |
| spellingShingle |
Electromyography Hand gestures K-nearest Neighbor (KNN) Support Vector Machine (SVM) EMG MyoWare Muscle Sensor Autodesk 3D Max software Prosthetic Biomaterials. Artificial intelligence. Machine learning. Rupom, Farhan Fuad Johan, Gazi Musa Al Jannat, Shafaitul Tamanna, Farjana Ferdousi EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Islam, Md. Motaharul |
| author_facet |
Islam, Md. Motaharul Rupom, Farhan Fuad Johan, Gazi Musa Al Jannat, Shafaitul Tamanna, Farjana Ferdousi |
| format |
Thesis |
| author |
Rupom, Farhan Fuad Johan, Gazi Musa Al Jannat, Shafaitul Tamanna, Farjana Ferdousi |
| author_sort |
Rupom, Farhan Fuad |
| title |
EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| title_short |
EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| title_full |
EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| title_fullStr |
EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| title_full_unstemmed |
EMG controlled bionic robotic arm using artificial intelligence and machine learning |
| title_sort |
emg controlled bionic robotic arm using artificial intelligence and machine learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14728 |
| work_keys_str_mv |
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| _version_ |
1814307266880339968 |