6 research outputs found
HOG and pairwise SVMs for neuromuscular activity recognition using instantaneous HD-sEMG images
The concept of neuromuscular activity recognition using instantaneous high-density surface electromyography (HD-sEMG) image opens up new avenues for the development of more fluid and natural muscle-computer interfaces. The state-of-the-art methods for instantaneous HD-sEMG image recognition achieve prominent performance using a computationally intensive deep convolutional networks (ConvNet) classifier, while very low performance is reported using the conventional classifiers. However, the conventional classifiers such as Support Vector Machines (SVM) can surpass ConvNet at producing optimal classification if well-behaved feature vectors are provided. This paper studies the question of extracting distinctive feature sets, thus propose to use Histograms of Oriented Gradient (HOG) as unique features for robust neuromuscular activity recognition, adopting pair wise SVMs as the classification scheme. The experimental results proved that the HOG represents unique features inside the instantaneous HD-sEMG image and fine-tuning the hyper- parameter of the pair wise SVMs, the recognition accuracy comparable to the more complex state of the art methods can be achieved
S-Convnet: A shallow convolutional neural network architecture for neuromuscular activity recognition using instantaneous high-density surface EMG images
The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) architecture and complex training schemes for HD-sEMG image recognition, which requires learning of ˃5.63 million(M) training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training dataset, as a result, it makes high-end resource-bounded and computationally expensive. To overcome this problem, we propose S-ConvNet models, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch using random-initialization. Without using any pre-trained models, our proposed S-ConvNet demonstrate very competitive recognition accuracy to the more complex state of the art, while reducing learning parameters to only ≈ 2M and using ≈ 12 × smaller dataset. The experimental results proved that the proposed S-ConvNet is highly effective for learning discriminative features for instantaneous HD-sEMG image recognition, especially in the data and high-end resource-constrained scenarios
All-ConvNet: A lightweight all CNN for neuromuscular activity recognition using instantaneous high-density surface EMG images
Neuromuscular activity recognition using low-resolution instantaneous high-density surface electromyography (HD-sEMG) images present a great challenge. The recent result shows the high potentiality and hence opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep ConvNet, which requires learning >5.63 million training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training datasets, as a result, it makes high-end resource bounded and computationally expensive. To overcome this problem, we propose a lightweight All-ConvNet model that consists solely of convolutional layers, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch through random initialization. Without using any pre-trained models, our proposed lightweight All-ConvNet demonstrate very competitive or even state of the art performance on a current benchmarks HD-sEMG dataset, while requires learning only ~460k training parameters and using ~12xsmaller dataset. The experimental results proved that the proposed lightweight All-ConvNet is highly effective for learning discriminative features for low-resolution instantaneous HD-sEMG image recognition and low-latency processing especially in the data and high-end resource constrained scenarios
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
Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm.
To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device’s velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals’ amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm