1,133 research outputs found
Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation
Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio
From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models
Multimodal hand gesture recognition (HGR) systems can achieve higher
recognition accuracy. However, acquiring multimodal gesture recognition data
typically requires users to wear additional sensors, thereby increasing
hardware costs. This paper proposes a novel generative approach to improve
Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial
Measurement Unit (IMU) signals. Specifically, we trained a deep generative
model based on the intrinsic correlation between forearm sEMG signals and
forearm IMU signals to generate virtual forearm IMU signals from the input
forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU
signals were fed into a multimodal Convolutional Neural Network (CNN) model for
gesture recognition. To evaluate the performance of the proposed approach, we
conducted experiments on 6 databases, including 5 publicly available databases
and our collected database comprising 28 subjects performing 38 gestures,
containing both sEMG and IMU data. The results show that our proposed approach
outperforms the sEMG-based unimodal HGR method (with increases of
2.15%-13.10%). It demonstrates that incorporating virtual IMU signals,
generated by deep generative models, can significantly enhance the accuracy of
sEMG-based HGR. The proposed approach represents a successful attempt to
transition from unimodal HGR to multimodal HGR without additional sensor
hardware
putEMG -- a surface electromyography hand gesture recognition dataset
In this paper, we present a putEMG dataset intended for evaluation of hand
gesture recognition methods based on sEMG signal. The dataset was acquired for
44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4
pinches, and idle). It consists of uninterrupted recordings of 24 sEMG channels
from the subject's forearm, RGB video stream and depth camera images used for
hand motion tracking. Moreover, exemplary processing scripts are also
published. putEMG dataset is available under Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at:
https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated
regarding sEMG amplitudes and gesture recognition performance. The
classification was performed using state-of-the-art classifiers and feature
sets. Accuracy of 90% was achieved for SVM classifier utilising RMS feature and
for LDA classifier using Hudgin's and Du's feature sets. Analysis of
performance for particular gestures showed that LDA/Du combination has
significantly higher accuracy for full hand gestures, while SVM/RMS performs
better for pinch gestures. Presented dataset can be used as a benchmark for
various classification methods, evaluation of electrode localisation concepts,
or development of classification methods invariant to user-specific features or
electrode displacement
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning
Gesture recognition using low-resolution instantaneous HD-sEMG images opens
up new avenues for the development of more fluid and natural muscle-computer
interfaces. However, the data variability between inter-session and
inter-subject scenarios presents a great challenge. The existing approaches
employed very large and complex deep ConvNet or 2SRNN-based domain adaptation
methods to approximate the distribution shift caused by these inter-session and
inter-subject data variability. Hence, these methods also require learning over
millions of training parameters and a large pre-trained and target domain
dataset in both the pre-training and adaptation stages. As a result, it makes
high-end resource-bounded and computationally very expensive for deployment in
real-time applications. To overcome this problem, we propose a lightweight
All-ConvNet+TL model that leverages lightweight All-ConvNet and transfer
learning (TL) for the enhancement of inter-session and inter-subject gesture
recognition performance. The All-ConvNet+TL model consists solely of
convolutional layers, a simple yet efficient framework for learning invariant
and discriminative representations to address the distribution shifts caused by
inter-session and inter-subject data variability. Experiments on four datasets
demonstrate that our proposed methods outperform the most complex existing
approaches by a large margin and achieve state-of-the-art results on
inter-session and inter-subject scenarios and perform on par or competitively
on intra-session gesture recognition. These performance gaps increase even more
when a tiny amount (e.g., a single trial) of data is available on the target
domain for adaptation. These outstanding experimental results provide evidence
that the current state-of-the-art models may be overparameterized for
sEMG-based inter-session and inter-subject gesture recognition tasks
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
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.Comment: The first two authors shared first authorship. The last three authors
shared senior authorship. 32 page
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