Deep Learning-based User Authentication with Surface EMG Images of Hand Gestures

2021 ◽  
Author(s):  
Qingqing Li ◽  
Zhirui Luo ◽  
Jun Zheng
Keyword(s):  
Deep Learning ◽  
User Authentication ◽  
Surface Emg ◽  
Hand Gestures

Leveraging shape and depth in user authentication from in-air hand gestures

2015 ◽  
Author(s):  
Jonathan Wu ◽  
James Christianson ◽  
Janusz Konrad ◽  
Prakash Ishwar
Keyword(s):  
User Authentication ◽  
Hand Gestures

scholarly journals Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Peng Liu ◽  
Xiangxiang Li ◽  
Haiting Cui ◽  
Shanshan Li ◽  
Yafei Yuan
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Algorithm ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Single Shot ◽  
Complex Scene ◽  
Hand Gestures ◽  
Benchmark Database

Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.

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 How does hand gestures in videos impact social media engagement - Insights based on deep learning

2021 ◽  
Vol 1 (2) ◽  
pp. 100036
Author(s):  
Kartik Anand ◽  
Siddhaling Urolagin ◽  
Ram Krishn Mishra
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hand Gestures ◽  
Social Media Engagement

scholarly journals 3D Hand Gestures Segmentation and Optimized Classification Using Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fawad Salam Khan ◽  
Mohd. Norzali Haji Mohd ◽  
Dur Muhammad Soomro ◽  
Susama Bagchi ◽  
M. Danial Khan
Keyword(s):  
Deep Learning ◽  
Hand Gestures

scholarly journals A Control System of SEMG Signal Based on Deep Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Training Model ◽  
Surface Emg ◽  
Cross Combination ◽  
Semg Signal ◽  
Engineering Technology ◽  
Emg Signal ◽  
Successful Design ◽  
Medical Theory

The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals.

scholarly journals Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1642 ◽  
Author(s):  
Ali Raza Asif ◽  
Asim Waris ◽  
Syed Omer Gilani ◽  
Mohsin Jamil ◽  
Hassan Ashraf ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Pattern Recognition Methods ◽  
Upper Limb Prosthesis ◽  
Biosignal Processing ◽  
Prosthesis Control

Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still insufficient for natural control. Recent advancements in deep learning have shown tremendous progress in biosignal processing. Multiple architectures have been proposed yielding high accuracies (>95%) for offline analysis, yet the delay caused due to optimization of the system remains a challenge for its real-time application. From this arises a need for optimized deep learning architecture based on fine-tuned hyper-parameters. Although the chance of achieving convergence is random, however, it is important to observe that the performance gain made is significant enough to justify extra computation. In this study, the convolutional neural network (CNN) was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture. Results showed that the learning rate set to either 0.0001 or 0.001 with 80-100 epochs significantly outperformed (p < 0.05) other considerations. In addition, it was observed that regardless of network configuration some motions (close hand, flex hand, extend the hand and fine grip) performed better (83.7% ± 13.5%, 71.2% ± 20.2%, 82.6% ± 13.9% and 74.6% ± 15%, respectively) throughout the course of study. So, a robust and stable myoelectric control can be designed on the basis of the best performing hand motions. With improved recognition and uniform gain in performance, the deep learning-based approach has the potential to be a more robust alternative to traditional machine learning algorithms.

scholarly journals A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3876 ◽  
Author(s):  
Tiantian Zhu ◽  
Zhengqiu Weng ◽  
Guolang Chen ◽  
Lei Fu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Mobile Devices ◽  
Real World ◽  
Large Scale ◽  
User Authentication ◽  
Mobile User ◽  
Learning System ◽  
Support Vector ◽  
Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

Sign in / Sign up

Export Citation Format

Share Document