Hand and elbow gesture recognition based on electromyography signal
This project intends to analyze and classify the Electromyography (EMG) signal of muscles that is involved in certain hand and elbow gestures. The Electromyography (EMG) data acquisition protocol is then outlined and performed where the recorded Electromyography (EMG) signal corresponds with cert...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | English English |
| 出版事項: |
2019
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/451/1/24p%20ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI.pdf http://eprints.uthm.edu.my/451/2/ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI%20WATERMARK.pdf |
| 要約: | This project intends to analyze and classify the Electromyography (EMG) signal of
muscles that is involved in certain hand and elbow gestures. The Electromyography
(EMG) data acquisition protocol is then outlined and performed where the recorded
Electromyography (EMG) signal corresponds with certain hand and elbow gestures.
Therefore, four hand gestures were targeted, “hand contraction, forearm rotation, wrist
extension and wrist flexion”. Thus, the EMG data that have been collected from 6
subjects are compared at a small demographic scale which is age and gender. Whereas,
the EMG signals are collected using the software Lab-Chart 7 with 2 channel and 5
electrodes. The pre-processing of the EMG raw signals is presented using a 6th order
Butterworth band pass filter, low and high pass filter with normalization. Furthermore,
the features are evaluated using Variance (VAR), Standard Deviation (SD) and Root
Mean Square (RMS) to test the significance of the features. Nevertheless, the K-Nearest
Neighbour (KNN) classifier is used in order to classify the EMG signals for hand
gestures. Lastly, the results from this project showed that the classifier has classified the
gestures with a low performance due to the fewer amounts of the subjects and some
other reasons. |
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