On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions

Kwon, Yongje; Dwivedi, Anany; McDaid, Andrew; Liarokapis, Minas

doi:10.1109/embc.2018.8512624

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…In our previous works, we proposed a learning scheme based on the RF regression method to map the myoelectric activations of the muscles of the forearm and the hand to the object's motion. We studied the optimal muscle selection for the sEMG-based decoding of these in-hand manipulation motions [6], [21]. Then we explored how the EMG signals vary across different subjects of different genders and with different hand sizes, assessing the decoding models' perfor-…”

Section: Introductionmentioning

confidence: 99%

Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers

Godoy

Dwivedi

Liarokapis

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for various applications, ranging from entertainment (EMG controlled games) to human assistance and human augmentation (EMG controlled prostheses and exoskeletons). For this, classical machine learning methods such as Random Forest (RF) models have been used to decode EMG signals. However, these methods depend on several stages of signal pre-processing and extraction of handcrafted features so as to obtain the desired output. In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, inhand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers. The results obtained are compared, in terms of accuracy and speed of decoding the motion, with RF-based models and Convolutional Neural Networks as a benchmark. The models are trained for 11 subjects in a motion-object specific and motion-object generic way, using the 10-fold crossvalidation procedure. This study shows that the performance of MuMIs can be improved by employing DL-based models with raw myoelectric activations instead of developing DL or classic machine learning models with handcrafted features.

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Section: Introductionmentioning

confidence: 99%

Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers

Godoy

Dwivedi

Liarokapis

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The first is to research the discrete motion which corresponds to static contraction of muscles through sEMG signal, such as keeping hands still or making the peace sign [10,11]. The second is to use the sEMG signal to predict the continuous motion changes of the joint which corresponds to the dynamic contraction of muscles, such as changes of joint torque and joint angle [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

Qing

Cai

et al. 2021

Sensors

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The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.

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“…In our previous work, we proposed a learning scheme that maps the myoelectric activations of the muscles of the hand and the forearm to the motion of an object [9] and optimized muscle selection [10]. We also explored effects of gender and hand sizes on object motion decoding [11] and compared manipulation of centered and off-centered mass objects while performing in-hand manipulation motions with real objects [12].…”

Section: Introductionmentioning

confidence: 99%

Electromyography-Based Decoding of Dexterous, In-Hand Manipulation of Objects: Comparing Task Execution in Real World and Virtual Reality

et al. 2021

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The increased use of Virtual and Augmented Reality based systems necessitates the development of more intuitive and unobtrusive means of interfacing. Over the last years, Electromyography (EMG) based interfaces have been employed for interaction with robotic and computer applications, but no studies have been carried out to investigate the continuous decoding of the effects of human motion (e.g., manipulated object behavior) in simulated and virtual environments. In this work, we compare the object motion decoding accuracy of an EMG based learning framework for two different dexterous manipulation scenarios: i) for simulated objects handled by a teleoperated model of a hand within a virtual environment and ii) for real, everyday life objects manipulated by the human hand. To do that, we utilize EMG activations from 16 muscle sites (9 on the hand and 7 on the forearm). The object motion decoding is formulated as a regression problem using the Random Forests methodology. A 5-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each model. The decoding accuracy for the real world is considerably higher than the virtual world. Each of the objects examined had a single manipulation motion that offered the highest estimation accuracy across both worlds. This study also shows that it is feasible to decode the object motions using just the myoelectric activations of the muscles of the forearm and the hand. This is particularly surprising since simulations lacked haptic feedback and the ability to account for other dynamic phenomena like friction and contact rolling.

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On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions

Cited by 9 publications

References 13 publications

Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers

Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions With Temporal Multichannel Vision Transformers

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

Electromyography-Based Decoding of Dexterous, In-Hand Manipulation of Objects: Comparing Task Execution in Real World and Virtual Reality

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