Hand movements classification based on Myo armband signals

Esaa, Raghad Radi; Jaber, Hanadi Abbas; Ameer, Abbas Abdul

doi:10.1109/icecie55199.2022.10000349

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Many machine learning algorithms are used for hand gesture recognition such as support vector machine (SVM) [9], [10], K-nearest neighbour (KNN), random forest [11], linear discriminant analysis (LDA) [12], artificial neural network (ANN) [13], and recently deep learning methods [14]- [16]. Features may be obtained in time, frequency scale, and time-frequency scale [17], [18].…”

Section:  Issn: 2252-8938mentioning

confidence: 99%

“…A classifier discriminates the signal patterns and classifies them into their categories. Several studies [6], [10], [28] have shown that pattern recognition (PR) based control depends entirely on the selection and extraction of high-quality features. Feature selection is considered a key to improving recognition accuracy.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In contrast to the strategies proposed by Hudgins [19], this work proposes a new combination of TD features that include average amplitude change (AAC), mean absolute deviation (MAD), root mean square (RMS), standard deviation (SD), mean absolute value (MAV), skewness (SKEW), and interquartile range (IQR). On the other hand, the parameters of the auto regressive (AR) model are combined with TD features to form an eight-time domain auto-regression features (TD-AR) [10], [18], [29]. The TD-AR features are extracted from the short-term EMG signals without using window techniques in order to reduce computational complexity.…”

Section: Time Domain With Auto Regression (Td-ar) Featuresmentioning

confidence: 99%

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Short-term hand gestures recognition based on electromyography signals

Essa

Jaber

Jasim

2023

IJ-AI

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Electromyography pattern recognition to predict limb movements can significantly enhance the control of the prosthesis. However, this technique has not yet been widely used in clinical practice. Improvements in the myoelectric pattern recognition (MPR) system can improve the functionality of the prosthesis. This study proposes new sets of time domain features to enhance the MPR control system. Three groups of features are evaluated, time domain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.

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Section:  Issn: 2252-8938mentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Time Domain With Auto Regression (Td-ar) Featuresmentioning

confidence: 99%

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Short-term hand gestures recognition based on electromyography signals

Essa

Jaber

Jasim

2023

IJ-AI

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Leveraging Inertial Information from a Single IMU for Human Daily Activity Recognition

Scattolini,

Tigrini,

Verdini

et al. 2024

2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Human Hand Movement Classification based on EMG Signal using different Feature Extractor

Shilaskar,

Bhatlawande,

Chavare

et al. 2024

Biomed. Pharmacol. J.

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Electromyography (EMG) based hand movement classification plays a significant role in various fields, namely in prosthetics, rehabilitation, biomechanics, etc. This paper presents the study of EMG-based hand movement classification of 3 human hand gestures (hand at rest, wrist flexion, and wrist extension). The dataset was officially collected from the University of California, Irvine (UCI) machine learning repository. The dataset contains 8 channels and 3 classes representing 3 human hand gestures, with 15000 rows of EMG data for each class. The dataset obtained was raw and unprocessed, to filter this dataset Notch and Butterworth filters were used. After filtering, the sliding window was performed. Various feature extraction techniques, namely frequency domain features (FD) and discrete wavelet transform (DWT) were applied separately on the window dataset and then accuracy was tested on different classifiers, namely random forest (RF), k- nearest neighbor (KNN), and decision tree (DT). As a novel approach, time domain (TD) and DWT extracted features were fused together and then given to the classifiers to test accuracy. Among all these feature extractors, the features extracted by FD provided the highest accuracy of 81.69 for the RF classifier.

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Hand movements classification based on Myo armband signals

Cited by 4 publications

References 24 publications

Short-term hand gestures recognition based on electromyography signals

Short-term hand gestures recognition based on electromyography signals

Leveraging Inertial Information from a Single IMU for Human Daily Activity Recognition

Human Hand Movement Classification based on EMG Signal using different Feature Extractor

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