Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures
In this paper four Machine Learning (ML) Algorithms have been implemented for the classification of four hand gestures using electromyography (EMG) dataset. The classifiers opted are Support Vector Machine (SVM), Random Forest (RF), Bagged tree and Extreme Gadient Boosting (XGBoost). The prediction...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2022-11-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/volume-32/fruct32/files/Ala.pdf |
| _version_ | 1811214841629638656 |
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| author | Shahed Alam Md Saif Kabir Mohammad Naveed Hossain Quazi Rian Hasnaine Md. Golam Rabiul Alam |
| author_facet | Shahed Alam Md Saif Kabir Mohammad Naveed Hossain Quazi Rian Hasnaine Md. Golam Rabiul Alam |
| author_sort | Shahed Alam |
| collection | DOAJ |
| description | In this paper four Machine Learning (ML) Algorithms have been implemented for the classification of four hand gestures using electromyography (EMG) dataset. The classifiers opted are Support Vector Machine (SVM), Random Forest (RF), Bagged tree and Extreme Gadient Boosting (XGBoost). The prediction accuracy of the machine learning algorithms were subsequently compared with Long Short-Term Memory (LSTM) which is a Deep learning based classification technique. Among the machine learning algorithms, XGBoost provided the highest accuracy of approximately 97% while LSTM provided a superior accuracy close to 99% which promises to provide the physiologically natural upper-limb movement control. In addition to the pursuit of improved accuracy in the research, the effect of removing the noisiest channel in the accuracy of the algorithms has been examined in order to decrease the volume of data processing. |
| first_indexed | 2024-04-12T06:11:16Z |
| format | Article |
| id | doaj.art-0c91a19caba24f0b92d669befd16693b |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-04-12T06:11:16Z |
| publishDate | 2022-11-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-0c91a19caba24f0b92d669befd16693b2022-12-22T03:44:41ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372022-11-01321222910.23919/FRUCT56874.2022.9953843Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand GesturesShahed Alam0Md Saif Kabir1Mohammad Naveed Hossain2Quazi Rian Hasnaine3Md. Golam Rabiul Alam4Brac University, BangladeshBrac University, BangladeshBrac University, BangladeshBrac University, BangladeshBrac University, BangladeshIn this paper four Machine Learning (ML) Algorithms have been implemented for the classification of four hand gestures using electromyography (EMG) dataset. The classifiers opted are Support Vector Machine (SVM), Random Forest (RF), Bagged tree and Extreme Gadient Boosting (XGBoost). The prediction accuracy of the machine learning algorithms were subsequently compared with Long Short-Term Memory (LSTM) which is a Deep learning based classification technique. Among the machine learning algorithms, XGBoost provided the highest accuracy of approximately 97% while LSTM provided a superior accuracy close to 99% which promises to provide the physiologically natural upper-limb movement control. In addition to the pursuit of improved accuracy in the research, the effect of removing the noisiest channel in the accuracy of the algorithms has been examined in order to decrease the volume of data processing.https://www.fruct.org/publications/volume-32/fruct32/files/Ala.pdfelectromyography (emg)myo gesture controlprosthesismachine learningdeep learning |
| spellingShingle | Shahed Alam Md Saif Kabir Mohammad Naveed Hossain Quazi Rian Hasnaine Md. Golam Rabiul Alam Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures Proceedings of the XXth Conference of Open Innovations Association FRUCT electromyography (emg) myo gesture control prosthesis machine learning deep learning |
| title | Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures |
| title_full | Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures |
| title_fullStr | Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures |
| title_full_unstemmed | Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures |
| title_short | Classification Accuracy Comparison between Machine Learning Algorithms and a Deep Learning Algorithm in Predicting Hand Gestures |
| title_sort | classification accuracy comparison between machine learning algorithms and a deep learning algorithm in predicting hand gestures |
| topic | electromyography (emg) myo gesture control prosthesis machine learning deep learning |
| url | https://www.fruct.org/publications/volume-32/fruct32/files/Ala.pdf |
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