2,516 research outputs found
Deep Residual Shrinkage Networks for EMG-based Gesture Identification
This work introduces a method for high-accuracy EMG based gesture
identification. A newly developed deep learning method, namely, deep residual
shrinkage network is applied to perform gesture identification. Based on the
feature of EMG signal resulting from gestures, optimizations are made to
improve the identification accuracy. Finally, three different algorithms are
applied to compare the accuracy of EMG signal recognition with that of DRSN.
The result shows that DRSN excel traditional neural networks in terms of EMG
recognition accuracy. This paper provides a reliable way to classify EMG
signals, as well as exploring possible applications of DRSN
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
Intersected EMG heatmaps and deep learning based gesture recognition
Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
In recent years, deep learning algorithms have become increasingly more
prominent for their unparalleled ability to automatically learn discriminant
features from large amounts of data. However, within the field of
electromyography-based gesture recognition, deep learning algorithms are seldom
employed as they require an unreasonable amount of effort from a single person,
to generate tens of thousands of examples.
This work's hypothesis is that general, informative features can be learned
from the large amounts of data generated by aggregating the signals of multiple
users, thus reducing the recording burden while enhancing gesture recognition.
Consequently, this paper proposes applying transfer learning on aggregated data
from multiple users, while leveraging the capacity of deep learning algorithms
to learn discriminant features from large datasets. Two datasets comprised of
19 and 17 able-bodied participants respectively (the first one is employed for
pre-training) were recorded for this work, using the Myo Armband. A third Myo
Armband dataset was taken from the NinaPro database and is comprised of 10
able-bodied participants. Three different deep learning networks employing
three different modalities as input (raw EMG, Spectrograms and Continuous
Wavelet Transform (CWT)) are tested on the second and third dataset. The
proposed transfer learning scheme is shown to systematically and significantly
enhance the performance for all three networks on the two datasets, achieving
an offline accuracy of 98.31% for 7 gestures over 17 participants for the
CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw
EMG-based ConvNet. Finally, a use-case study employing eight able-bodied
participants suggests that real-time feedback allows users to adapt their
muscle activation strategy which reduces the degradation in accuracy normally
experienced over time.Comment: Source code and datasets available:
https://github.com/Giguelingueling/MyoArmbandDatase
EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS
In this paper, deep learning-based hand gesture recognition using surface EMG signals is presented. We use Principal component analysis (PCA) to reduce the data set. Here a threshold-based approach is also proposed to select the principal components (PCs). Then the Continuous wavelet transform (CWT) is carried out to prepare the time-frequency representation of images which is used as the input of the classifier. A very deep convolutional neural network (CNN) is proposed as the gesture classifier. The classifier is trained on 10-fold cross-validation framework and we achieve average recognition accuracy of 99.44%, sensitivity of 97.78% and specificity of 99.68% respectively
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.Comment: 8 pages, accepted by AAAI201
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