Study on the emg-based grasp gesture classification using deep learning and application to active prosthetics

被引：3

作者：

Jo Y.U. ^{[1
]}

Oh D.C. ^{[1
]}

机构：

[1] Department of Biomedical Engineering, Konyang University

来源：

Journal of Institute of Control, Robotics and Systems | 2019年 / 25卷 / 03期

关键词：

Active prosthetics; CNN; Hand gesture of grasping; Robot hands; SEMG sensor;

D O I：

10.5302/J.ICROS.2019.19.8002

中图分类号：

学科分类号：

摘要：

In this paper, we extract the learning and test data for the "hand gesture of grasping" through the sEMG sensor, execute the Deep Learning CNN (convolutional neural network) algorithm by appropriately modifying it, and classify typical hand gestures that catch objects with a classification success rate (accuracy) of approximately 93.8%. In addition, we have constructed a system that can operate robot hands in real time from these classified commands to make active prosthetics. © ICROS 2019.

引用

页码：229 / 234

页数：5

共 50 条

[41] EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks
Xia, Peng
Hu, Jie
Peng, Yinghong
ARTIFICIAL ORGANS, 2018, 42 (05) : E67 - E77
[42] Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing
Moin, Ali
Zhou, Andy
Benatti, Simone
Rahimi, Abbas
Benini, Luca
Rabaey, Jan M.
2019 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS 2019), 2019,
[43] EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks
Neacs, Ana
Pesquet, Jean-Christophe
Burileanu, Corneliu
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2024, 15 (02) : 1 - 25
[44] EMG based Gesture Recognition using Machine Learning
Anil, Nikitha
Sreeletha, S. H.
PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, : 1560 - 1564
[45] Federated Learning-Enhanced Edge Deep Learning Model for EMG-Based Gesture Recognition in Real-Time Human-Robot Interaction
Zafar, Muhammad Hamza
Moosavi, Syed Kumayl Raza
Sanfilippo, Filippo
IEEE SENSORS JOURNAL, 2025, 25 (05) : 9139 - 9151
[46] Wearable EMG-Based Gesture Recognition Systems During Activities of Daily Living: An Exploratory Study
Chang, Jason
Phinyomarki, Angkoon
Bateman, Scott
Scheme, Erik
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 3448 - 3451
[47] EMG-based posture classification using a convolutional neural network for a myoelectric hand
Yamanoi, Yusuke
Ogiri, Yosuke
Kato, Ryu
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 55
[48] Deep Learning Based Surface EMG Hand Gesture Classification for Low-Cost Myoelectric Prosthetic Hand
Nahid, Nazmun
Rahman, Arafat
Ahad, M. A. R.
2020 JOINT 9TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2020 4TH INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR), 2020,
[49] A portable MIDI controller using EMG-based individual finger motion classification
Bitar, Fadi
Madi, Nasr
Ramly, Edmond
Saghir, Mazen
Karameh, Fadi
2007 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE, 2007, : 138 - 141
[50] LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition
Zhang, Haotian
Qu, Hang
Teng, Long
Tang, Chak-Yin
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 : 4749 - 4759

← 1 2 3 4 5 →