Deep Augmentation for Electrode Shift Compensation in Transient High-density sEMG: Towards Application in Neurorobotics

Sun, Tianyun; Libby, Jacqueline; Rizzo, John-Ross; Atashzar, S. Farokh

doi:10.1109/iros47612.2022.9981786

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…In this paper, the time window of 1.2 seconds is included from each repetition, which primarily contains the transient and more dynamic phase of the contraction (more details in [13], [24]). This is done to enhance the system's agility in responding to user intentions.…”

Section: B Data Preprocessingmentioning

confidence: 99%

“…Beginning with the two full 8 × 8 grids, a kernel window of 1 × 6 × 6 × 1 is selected and the patches extracted with stride one. This procedure is synonymous with the combination of one-step and two-step shifting described by [13].…”

Section: Data Augmentationmentioning

confidence: 99%

“…The 3D CNN model discussed in this paper follows the settings from [13]. The model consists of a block with five 3D convolutional layers and a classifier with two dense layers.…”

Section: A 3d Cnnmentioning

confidence: 99%

“…Current sEMG is inhibited from mass adoption due to poor spatial resolution of decoding, sensitivity to electrode shift, sensitivity to orientation of placement, unpredictable signal variability, and channel dropout [10]- [16]. While there have been some recent attempts at combating HD-sEMG's shortcomings, notably electrode shift [13], addressing channel dropout is still an open and important problem.…”

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Prediction via Transient-phase sEMG using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

Tyacke

Reddy²,

Feng

et al. 2022

Preprint

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There has been an accelerated surge to utilize the deep neural network for decoding central and peripheral activations of the human's nervous system to boost up the spatiotemporal resolution of neural interfaces used in neurorobotics. Such algorithmic solutions are motivated for use in human-centered robotic systems, such as neurorehabilitation, prosthetics, and exoskeletons. These methods are proved to achieve higher accuracy on individual data when compared with the conventional machine learning methods but are also challenged by their assumption of having access to massive training samples. Objective: In this letter, we propose Dilated Efficient CapsNet to improve the predictive performance when the available individual data is very minimum and not enough to train an individualized network for controlling a personalized robotic system. Method: We proposed the concept of transfer learning using a new design of the dilated efficient capsular neural network to relax the need of having access to massive individual data and utilize the field knowledge which can be learned from a group of participants. In addition, instead of using complete sEMG signals, we only use the transient phase, reducing the volume of training samples to 20\% of the original and maximizing the agility. Results: In experiments, we validate our model performance with various amounts of injected personalized training data (25%-100% of transient phase) that is segmented once by time and once by repetition. The results of this paper support the use of transfer learning using a dilated capsular neural network and show that with the use of such a model, the knowledge domain learned on a small number of subjects can be utilized to minimize the need for new data of new subjects while focusing only on the transient phase of contraction (which is a challenging neural interfacing problem).

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Section: B Data Preprocessingmentioning

confidence: 99%

Section: Data Augmentationmentioning

confidence: 99%

“…The 3D CNN model discussed in this paper follows the settings from [13]. The model consists of a block with five 3D convolutional layers and a classifier with two dense layers.…”

Section: A 3d Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Prediction via Transient-phase sEMG using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

Tyacke

Reddy²,

Feng

et al. 2022

Preprint

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Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

Montazerin

Rahimian

Naderkhani

et al. 2023

Sci Rep

Self Cite

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Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based Hand Gesture Recognition framework referred to as $$\text {CT-HGR}$$ CT-HGR , which employs a vision transformer network to conduct hand gesture recognition using high-density surface EMG (HD-sEMG) signals. Taking advantage of the attention mechanism, which is incorporated into the transformer architectures, our proposed $$\text {CT-HGR}$$ CT-HGR framework overcomes major constraints associated with most of the existing deep learning models such as model complexity; requiring feature engineering; inability to consider both temporal and spatial information of HD-sEMG signals, and requiring a large number of training samples. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. $$\text {CT-HGR}$$ CT-HGR can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the $$\text {CT-HGR}$$ CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the $$\text {CT-HGR}$$ CT-HGR is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed $$\text {CT-HGR}$$ CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. Our results are obtained via 5-fold cross-validation by first applying the proposed framework on the dataset of each subject separately and then, averaging the accuracies among all the subjects. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The $$\text {CT-HGR}$$ CT-HGR achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image. The proposed model is statistically compared with a 3D Convolutional Neural Network (CNN) and two different variants of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models. The accuracy results for each of the above-mentioned models are paired with their precision, recall, F1 score, required memory, and train/test times. The results corroborate effectiveness of the proposed $$\text {CT-HGR}$$ CT-HGR framework compared to its counterparts.

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From Unstable Electrode Contacts to Reliable Control: A Deep Learning Approach for HD-sEMG in Neurorobotics

Tyacke,

Gupta,

Patel

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Deep Augmentation for Electrode Shift Compensation in Transient High-density sEMG: Towards Application in Neurorobotics

Cited by 4 publications

References 27 publications

Hand Gesture Prediction via Transient-phase sEMG using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

Hand Gesture Prediction via Transient-phase sEMG using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals

From Unstable Electrode Contacts to Reliable Control: A Deep Learning Approach for HD-sEMG in Neurorobotics

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