Controlling 3D Model of Human Hand Exploiting Synergistic Activation of The Upper Limb Muscles

Serdana, Firman Isma

doi:10.1109/ies55876.2022.9888488

Cited by 2 publications

(6 citation statements)

References 24 publications

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“…From examining Figure 6, it is evident that most studies utilized eight-channel EMG data, which represents the maximum number of channels used, excluding the study published by the authors of [48]. This study stands out for utilizing a high-definition device From examining Figure 6, it is evident that most studies utilized eight-channel EMG data, which represents the maximum number of channels used, excluding the study published by the authors of [48]. This study stands out for utilizing a high-definition device with an 8 × 16 channel matrix.…”

Section: Data Acquisition and Devicesmentioning

confidence: 99%

“…This study stands out for utilizing a high-definition device with an 8 × 16 channel matrix. In the study published by the authors of [48], the researchers achieved a 78% accuracy rate using a relatively compact database consisting of only 120 entries, spanning 4 subjects and 10 classes. When comparing the study published by the authors of [48] with other studies based on dataset size, two papers stood out due to their smaller dataset sizes and their exclusive reliance on EMG sensors.…”

Section: Data Acquisition and Devicesmentioning

confidence: 99%

“…In the study published by the authors of [48], the researchers achieved a 78% accuracy rate using a relatively compact database consisting of only 120 entries, spanning 4 subjects and 10 classes. When comparing the study published by the authors of [48] with other studies based on dataset size, two papers stood out due to their smaller dataset sizes and their exclusive reliance on EMG sensors. Both of the studies published by the authors of [63,92] achieved accuracies exceeding 90%, which is higher than that obtained by the authors of [48].…”

Section: Data Acquisition and Devicesmentioning

confidence: 99%

“…When comparing the study published by the authors of [48] with other studies based on dataset size, two papers stood out due to their smaller dataset sizes and their exclusive reliance on EMG sensors. Both of the studies published by the authors of [63,92] achieved accuracies exceeding 90%, which is higher than that obtained by the authors of [48]. However, it is essential to approach these results with caution.…”

Section: Data Acquisition and Devicesmentioning

confidence: 99%

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Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Ben Haj Amor,

El Ghoul,

Jemni

2023

Sensors

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The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.

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Section: Data Acquisition and Devicesmentioning

confidence: 99%

Section: Data Acquisition and Devicesmentioning

confidence: 99%

Section: Data Acquisition and Devicesmentioning

confidence: 99%

Section: Data Acquisition and Devicesmentioning

confidence: 99%

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Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Ben Haj Amor,

El Ghoul,

Jemni

2023

Sensors

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“…The purpose of Serdana [1] study is to propose a new approach to proportional hand control using high-density electromyography (HD-EMG). Previous research has demonstrated the potential of HD-EMG for controlling hand and finger movements [16].…”

Section: Introductionmentioning

confidence: 99%

Using High Density EMG to Proportionally Control 3D Model of Human Hand

Serdana¹,

Muceli²,

Farina³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Control of human hand using surface electromyography (EMG) is already established in various mechanisms, but proportionally controlling magnitudes degrees of freedom (DOF) of humanoid hand model is still highly developed in recent years. This paper proposes another method to achieve a proportional estimation and control of humanâ€™s hand multiple DOFs. Gestures in the form of American Sign Language (ABCDFIKLOW) were chosen as the targets, of which ten alphabetical gestures were specifically used following their clarity on its 3D model. Then the dataset of the movements gestures was simultaneously recorded using High-density electromyography (HD-EMG) and motion capture system. Sensor placements were on intrinsic - extrinsic muscles for HD-EMG and finger joints for the motion capture system. To derive the proportional control in time series between both datasets (HD-EMG and kinematics data), neural network (NN) and k-Nearest Neighbour were used. The models produced around 70-95 % (R index) accuracy for the eleven DOFs in four healthy subjectsâ€™ hand. kNNâ€™s performance was better than NN, even if the input features were reduced either using manual selections or principal component analysis (PCA). The time series controls could also identify most sign language gestures (9 of 10), with difficulty was given on O gesture. The false interpretation was because of nearly identical muscleâ€™s EMG and kinematics data between O and C. This paper intends to extend its conference version [1] by adding more in-depth Results and Discussion along making other sections more comprehensive.

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Controlling 3D Model of Human Hand Exploiting Synergistic Activation of The Upper Limb Muscles

Cited by 2 publications

References 24 publications

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Using High Density EMG to Proportionally Control 3D Model of Human Hand

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