Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier

2018 ◽  
Vol 46 ◽  
pp. 121-130 ◽  
Author(s):  
Mahmoud Tavakoli ◽  
Carlo Benussi ◽  
Pedro Alhais Lopes ◽  
Luis Bica Osorio ◽  
Anibal T. de Almeida
Keyword(s):  
Gesture Recognition ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Channel Surface ◽  
Double Channel

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

Surface EMG-Based Hand Gesture Recognition via Hybrid and Dilated Deep Neural Network Architectures for Neurorobotic Prostheses

2020 ◽  
Vol 05 (01n02) ◽  
pp. 2041001 ◽  
Author(s):  
Elahe Rahimian ◽  
Soheil Zabihi ◽  
Seyed Farokh Atashzar ◽  
Amir Asif ◽  
Arash Mohammadi
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Network Architectures ◽  
Spatial Features ◽  
Deep Model ◽  
Machine Interface ◽  
Fully Connected

Motivated by the potentials of deep learning models in significantly improving myoelectric control of neuroprosthetic robotic limbs, this paper proposes two novel deep learning architectures, namely the [Formula: see text] ([Formula: see text]) and the [Formula: see text] ([Formula: see text]), for performing Hand Gesture Recognition (HGR) via multi-channel surface Electromyography (sEMG) signals. The work is aimed at enhancing the accuracy of myoelectric systems, which can be used for realizing an accurate and resilient man–machine interface for myocontrol of neurorobotic systems. The HRM is developed based on an innovative, unconventional, and particular hybridization of two parallel paths (one convolutional and one recurrent) coupled via a fully-connected multilayer network acting as the fusion center providing robustness across different scenarios. The hybrid design is specifically proposed to treat temporal and spatial features in two parallel processing pipelines and to augment the discriminative power of the model to reduce the required computational complexity and construct a compact HGR model. We designed a second architecture, the [Formula: see text], as a compact architecture. It is worth mentioning that efficiency of a designed deep model, especially its memory usage and number of parameters, is as important as its achievable accuracy in practice. The [Formula: see text] has significantly less memory requirement in training when compared to the HRM due to implementation of novel dilated causal convolutions that gradually increase the receptive field of the network and utilize shared filter parameters. The NinaPro DB2 dataset is utilized for evaluation purposes. The proposed [Formula: see text] significantly outperforms its counterparts achieving an exceptionally-high HGR performance of [Formula: see text]%. The TCNM with the accuracy of [Formula: see text]% also outperforms existing solutions while maintaining low computational requirements.

scholarly journals putEMG—A Surface Electromyography Hand Gesture Recognition Dataset

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3548 ◽  
Author(s):  
Piotr Kaczmarek ◽  
Tomasz Mańkowski ◽  
Jakub Tomczyński
Keyword(s):  
Gesture Recognition ◽  
Motion Tracking ◽  
Recognition Performance ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Classification Methods ◽  
Hand Gestures ◽  
Semg Signal ◽  
Feature Sets

In this paper, we present a putEMG dataset intended for the 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. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An 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. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.

Surface EMG hand gesture recognition system based on PCA and GRNN

2019 ◽  
Vol 32 (10) ◽  
pp. 6343-6351 ◽  
Author(s):  
Jinxian Qi ◽  
Guozhang Jiang ◽  
Gongfa Li ◽  
Ying Sun ◽  
Bo Tao
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture

A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

2018 ◽  
Vol 38 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Wan-Ting Shi ◽  
Zong-Jhe Lyu ◽  
Shih-Tsang Tang ◽  
Tsorng-Lin Chia ◽  
Chia-Yen Yang
Keyword(s):  
Gesture Recognition ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Preliminary Study
Sign in / Sign up

Export Citation Format

Share Document