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sEMG-Based Gesture Classifier for a Rehabilitation Glove

Dorin Copaci, Janeth Arias, Marcos Gómez-Tomé, Luis Moreno, Dolores Blanco
2022 Frontiers in Neurorobotics  
The online results obtained with this architecture represent a promising solution for hand gesture recognition (98.7% accuracy) in sEMG signal classification.  ...  Human hand gesture recognition from surface electromyography (sEMG) signals is one of the main paradigms for prosthetic and rehabilitation device control.  ...  A novel hand gesture classifier for sEMG based on a neural network architecture (a combination between the Bayesian neural network, pattern recognition networks, and layer recurrent network) is developed  ... 
doi:10.3389/fnbot.2022.750482 pmid:35706550 pmcid:PMC9190783 fatcat:6fnyhjbbfjcfhllgpcl6te6uty

Soft-Wearable Device for the Estimation of Shoulder Orientation and Gesture [chapter]

Aldo F. Contreras-González, José Luis Samper-Escudero, David Pont-Esteban, Francisco Javier Sáez-Sáez, Miguel Ángel Sánchez-Urán, Manuel Ferre
2020 Lecture Notes in Computer Science  
The use of Time Series Networks (TSN) to estimate the arm orientation, and a pattern recognition method for the estimation of the classification of the gesture are proposed.  ...  It is demonstrated that it is possible to know the orientation of the shoulder, and that the algorithm is capable of recognising the five gestures proposed with two different configurations.  ...  For gesture classification, the condensed data from the flex sensors is taken along with the sEMG data in order to train a pattern recognition neural network.  ... 
doi:10.1007/978-3-030-58147-3_41 fatcat:asyghylyyvgbtoohhnlumivkm4

Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data

Philipp Koch, Nele Brugge, Huy Phan, Marco Maass, Alfred Mertins
2019 ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
For many applications of hand gesture recognition, a delayfree, affordable, and mobile system relying on body signals is mandatory.  ...  Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid delays.  ...  However, in general it is desirable for hand gesture recognition systems to be cheap and free of delay.  ... 
doi:10.1109/icassp.2019.8682986 dblp:conf/icassp/KochBPMM19 fatcat:qduk2qzxv5dqzhvw2bxvqul7na

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features [article]

Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin, Erik Scheme
2019 arXiv   pre-print
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  ...  Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition.  ...  Transfer learning for semg hand gestures recognition using convolutional neural networks.  ... 
arXiv:1912.00283v1 fatcat:wjirojfedbho5ouu2nkmjpjyqq

Data Augmentation of Surface Electromyography for Hand Gesture Recognition

Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras
2020 Sensors  
Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.  ...  The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability.  ...  on hand gesture recognition.  ... 
doi:10.3390/s20174892 pmid:32872508 pmcid:PMC7506981 fatcat:dgxskbldhza3xbjxeisf7mn5pe

A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles

Yina Wang, Liwei Zheng, Junyou Yang, Shuoyu Wang
2022 Sensors  
neural network can continuously predict hand grip at different wrist angles in real-time.  ...  network based on the dynamic window is proposed to recognize wrist joints.  ...  [15] introduced the concept of a sEMG composed of a high-density sEMG signal space, proposed a classification scheme of gesture recognition based on a sEMG and convolution neural network, and achieved  ... 
doi:10.3390/s22052002 pmid:35271152 pmcid:PMC8914750 fatcat:7eqw3azudvcwdomyvvl3pjrxie

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features

Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin, Erik Scheme
2020 Frontiers in Bioengineering and Biotechnology  
Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition.  ...  Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using  ...  As such, the learned features extracted from the network can be thought of as general features (and to a certain extent subject-independent) for the task of sEMG-based hand gesture recognition.  ... 
doi:10.3389/fbioe.2020.00158 pmid:32195238 pmcid:PMC7063031 fatcat:h6upj6a6sfey7cslfgpfpayft4

Front Matter: Volume 9875

2015 Eighth International Conference on Machine Vision (ICMV 2015)  
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, 04,  ...  9875 0E 3D fast wavelet network model-assisted 3D face recognition [9875-1] 9875 0F Hand posture recognizer based on separator wavelet networks [9875-8] 9875 0G Robust head pose estimation using  ...  -25] 9875 0K Toward an optimal convolutional neural network for traffic sign recognition [9875-29] 9875 0L Improving neural network performance on SIMD architectures [9875-32] 9875 0M Application  ... 
doi:10.1117/12.2230077 dblp:conf/icmv/X15 fatcat:jwyltqnva5flpc3y3m3hrpqx5i

Table of Contents

2022 IEEE journal of biomedical and health informatics  
Moradi 1708 Fatigue-Sensitivity Comparison of sEMG and A-Mode Ultrasound based Hand Gesture Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Zheng 1872 FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jbhi.2022.3159171 fatcat:n4v32gnznnht7mcjyakuhwenky

Multimodal Human Hand Motion Sensing and Analysis -A Review

Yaxu Xue, Zhaojie Ju, Kui Xiang, Jing Chen, Honghai Liu
2018 IEEE Transactions on Cognitive and Developmental Systems  
for hand motion sensing, including contact-based and non-contact-based approaches, are discussed with comparisons with their pros and cons; then, the state-of-theart analysis methods are introduced, with  ...  Firstly, the nature of human hand motions is discussed in terms of simple motions, such as grasps and gestures, and complex motions, e.g. in-hand manipulations and re-grasps; secondly, different techniques  ...  Bouchrika et al. introduced a wavelet network classifier and a NN classifier learning algorithm to realize the interaction with the computer by hand gesture recognition [80] .  ... 
doi:10.1109/tcds.2018.2800167 fatcat:ojznwvn3gzg7rgnfq7yg3wf4jm

A systematic review on hand gesture recognition techniques, challenges and applications

Mais Yasen, Shaidah Jusoh
2019 PeerJ Computer Science  
With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an  ...  Results The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied  ...  using low complexity recurrent neural network (RNN) algorithms for wearable devices, the first was based on video signal and uses convolutional neural network (CNN) with RNN for classification, and the  ... 
doi:10.7717/peerj-cs.218 pmid:33816871 pmcid:PMC7924500 fatcat:5wsggdsqxzaq7ixayilmfyfoqu

Table of Contents

2020 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA)  
Network for Gait Recognition And Demographic Factor Prediction Based on Plantar Pressure Images The Real Time Motion Pattern Recognition of Lower Limb Based on sEMG signals 407 Xiao Wang, Shichun Yang  ...  on Deformable Cascade Network 269 5247 Airborne Software Testing Technology Analysis Based on Fault Injection 279 Li Yu, Liu Jia, Li GuoDong, Li Xin 5248 Drug Resistance Prediction of Non-small  ... 
doi:10.1109/iciba50161.2020.9277169 fatcat:yaxrhl3iafa4hmonirxzrdawgy

Hand Pose Recognition Using Parallel Multi Stream CNN

Iram Noreen, Muhammad Hamid, Uzma Akram, Saadia Malik, Muhammad Saleem
2021 Sensors  
With the increase of hand-pose-based applications, new challenges in this domain have also emerged.  ...  In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures.  ...  [26] also presented a CNN-based hand gesture recognition approach for video data.  ... 
doi:10.3390/s21248469 pmid:34960562 pmcid:PMC8708730 fatcat:6zjdtwffufccvoyq77yl3ln3vq

A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models

Pranesh Gopal, Amandine Gesta, Abolfazl Mohebbi
2022 Sensors  
If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities.  ...  Investigating the trend of varying sliding window on feature-based and non-feature-based classification model revealed interesting correlation with the level of amputation.  ...  Non-feature-based learning using a Convolutional Neural Network (CNN) for hand gesture classification using NinaPro dataset. Figure 4 . 4 Figure 4.  ... 
doi:10.3390/s22103650 pmid:35632058 fatcat:v5gbuasghbgjnouhtlz46k7mlm

Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos [chapter]

Richa Golash, Yogendra Kumar Jain
2022 Artificial Intelligence  
The framework integrates the unique features of the Faster Region-based Convolutional Neural Network (Faster R-CNN) built on Residual Network and Scale-Invariant Feature Transform (SIFT) algorithm.  ...  The empirical results shown in the chapter demonstrate that the approach can withstand the intrinsic as well as extrinsic challenges associated with visual tracking of hand gestures in RGB videos.  ...  [9] , surface electromyography (sEMG) as wearable sensors and Artificial Neural Network (ANN) as classifiers are the most preferable choices in hand gesture recognition.  ... 
doi:10.5772/intechopen.103170 fatcat:7r2r3twnjfbkzj5iwx6mhzj6zy
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