Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

McCool, Paul; Petropoulakis, Lykourgos; Soraghan, John J.; Chatlani, Navin

doi:10.1016/j.bspc.2014.12.001

Cited by 15 publications

(5 citation statements)

References 27 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the pattern recognition-based control approach, a classifier trained with supervised learning was employed to map sEMG activity to one of the predefined classes that correspond to different control commands. In the past decades, many methods have been proposed to design a sEMG pattern recognition-based interface, some of which have achieved high accuracy with many classes in a laboratory environment [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network

Chen

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.

show abstract

Section: Introductionmentioning

confidence: 99%

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network

Chen

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…However, as an alternative to the pre-processing algorithms for contaminant type identification and interference removal, some researchers aim to propose techniques for EMG signal recovering (Machado et al 2019;De Moura & Balbinot 2018) and system robustness improvement to contaminants (McCool et al 2015;Teh & Hargrove 2021;Triwiyanto et al 2018). Machado et al (2019) and De Moura and Balbinot (2018) presented virtual sensor-based strategies to recover the information of a contaminated channel.…”

Section: Analysis Of the Presence Of Contaminants In The Semg Signalmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

View full text Add to dashboard Cite

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

show abstract

“…In recent decades, many gesture recognition methods based on sEMG signals have been proposed [ 18 , 19 , 20 ]. The current methods can be summarized into two categories.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans

Chen,

Yang,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user’s gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.

show abstract

Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

Cited by 15 publications

References 27 publications

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans

Contact Info

Product

Resources

About