EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark, Angkoon; Scheme, Erik

doi:10.3390/bdcc2030021

Cited by 174 publications

(97 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, sEMG-based gesture recognition literature primarily focuses on feature engineering as a way to increase the information density of the signal to improve gesture discrimination (Oskoei and Hu, 2007;Scheme and Englehart, 2011b;Phinyomark et al, 2012a). In the last few years, however, researchers have started leveraging deep learning (Atzori et al, 2016;Allard et al, 2016;Phinyomark and Scheme, 2018a), shifting the paradigm from feature engineering to feature learning.…”

Section: Introductionmentioning

confidence: 99%

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Côté-Allard

Campbell

Phinyomark

et al. 2020

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training.Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.

show abstract

Section: Introductionmentioning

confidence: 99%

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Côté-Allard

Campbell

Phinyomark

et al. 2020

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, application of deep learning algorithms to detect abnormal EMG patterns appears more promising [85], and performs well with EMG acquired directly from the muscles. The main issue in clinical application of deep learning is its real-time implementation.…”

Section: Emg Methods Pros Consmentioning

confidence: 99%

A Review of EMG Techniques for Detection of Gait Disorders

Singh¹,

Iqbal²,

White³

et al. 2019

Artificial Intelligence - Applications in Medicine and Biology

View full text Add to dashboard Cite

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices.

show abstract

“…In this study, our posture classification was performed by a convolutional neural network (CNN) with RMS values of sEMG signals as the inputs. It is the most commonly used for sEMG classification [31,32]. The CNN was used to classify postures with sEMG signals extracted from the chest and back.…”

Section: Posture Classification For Control Prosthesismentioning

confidence: 99%

Fabric Vest Socket with Embroidered Electrodes for Control of Myoelectric Prosthesis

Lee

Jamil

Kim

et al. 2020

Sensors

View full text Add to dashboard Cite

Myoelectric prostheses assist users to live their daily lives. However, the majority of users are primarily confined to forearm amputees because the surface electromyography (sEMG) that understands the motion intents should be acquired from a residual limb for control of the myoelectric prosthesis. This study proposes a novel fabric vest socket that includes embroidered electrodes suitable for a high-level upper amputee, especially for shoulder disarticulation. The fabric vest socket consists of rigid support and a fabric vest with embroidered electrodes. Several experiments were conducted to verify the practicality of the developed vest socket with embroidered electrodes. The sEMG signals were measured using commercial Ag/AgCl electrodes for a comparison to verify the performance of the embroidered electrodes in terms of signal amplitudes, the skin-electrode impedance, and signal-to-noise ratio (SNR). These results showed that the embroidered electrodes were as effective as the commercial electrodes. Then, posture classification was carried out by able-bodied subjects for the usability of the developed vest socket. The average classification accuracy for each subject reached 97.92%, and for all the subjects it was 93.2%. In other words, the fabric vest socket with the embroidered electrodes could measure sEMG signals with high accuracy. Therefore, it is expected that it can be readily worn by high-level amputees to control their myoelectric prostheses, as well as it is cost effective for fabrication as compared with the traditional socket.

show abstract

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Cited by 174 publications

References 108 publications

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

A Review of EMG Techniques for Detection of Gait Disorders

Fabric Vest Socket with Embroidered Electrodes for Control of Myoelectric Prosthesis

Contact Info

Product

Resources

About