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Multi-Session Surface Electromyogram Signal Database for Personal Identification

Jin-Su Kim, Cheol-Ho Song, EunSang Bak, Sung-Bum Pan
2022 Sustainability  
Therefore, in contrast to generic personal identification, which uses only a piece of registered information, the sEMG changes the registered information in a personal identification method.  ...  In order to solve the problems of DBs, this paper describes a method for constructing a multi-session sEMG DB for many subjects.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su14095739 fatcat:b2ql2gconbc3rat6rqc3ppitg4

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Ga-Young Choi, Chang-Hee Han, Hyung-Tak Lee, Nam-Jong Paik, Won-Seok Kim, Han-Jeong Hwang
2021 Journal of NeuroEngineering and Rehabilitation  
The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative  ...  Methods EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task.  ...  An EEG device is required for the use of the proposed motor-hotspot-identification approach based on a machine learning technique.  ... 
doi:10.1186/s12984-021-00972-7 pmid:34930380 pmcid:PMC8686235 fatcat:wtbyy25wljaz5ppfmn32lljkxy

EEG Based Four Class Human Limb Movement Detection by Mel Frequency Cepstral Coefficients and Quadratic Multi-Class Support Vector Machine

Nasir Rashid, Javaid Iqbal, Umar Shahbaz Khan, Mohsin Islam Tiwana, Amir Hamza
2020 Journal of Engineering and Applied Sciences (JEAS) University of Engineering and Technology Peshawar  
Conflict of interest The authors have declared no conflict of interest.  ...  approval of the version to be sub-mitted for publishing.  ...  We also employed Linear Multi-class Support Vector Machine and Cubic Multi-class Support Vector Machine which generated comparable results.  ... 
doi:10.17582/journal.jeas/39.1.116.126 fatcat:yydxkids6ncppm5f6mrzpqkrqy

Brain Wave Recognition of Word Imagination Based on Support Vector Machines

Xiaomin Zhang, Yongping Li, Xushan Peng
2016 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Researchers have studied the right and left hand movement imagination, but for the study of word recognition, especially Chinese words' EEG identification research, there are no researches on them.  ...  Brain-computer interface is a kind of new information exchange and control technology, and the key is to give an accurate and timely identification of electrical machine characteristic parameters of thinking  ...  Y201432724), the Foundation of Zhejiang Science Committee (Grant No. 2014C31160), and the Foundation of Ningbo Science Bureau (Grant No. 2015C10050).  ... 
doi:10.12928/telkomnika.v14i3a.4391 fatcat:hbbvqsnu55dylcucgt7upeobuu

Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch [article]

Akriti Verma, Valeh Moghaddam, Adnan Anwar
2021 arXiv   pre-print
Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation.  ...  This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature.  ...  For the purpose of testing the identification, a multi-class prediction problem, the accuracy of the machine learning models was used as the metric.  ... 
arXiv:2110.03149v1 fatcat:43njkb25cze3vdsjjcv5b7myga

Data-Driven Behavioural Biometrics for Continuous and Adaptive User Verification Using Smartphone and Smartwatch

Akriti Verma, Valeh Moghaddam, Adnan Anwar
2022 Sustainability  
Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation.  ...  This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su14127362 fatcat:bkwioghacnda3dh75ezfk4x2iq

Real Time Action Recognition in Surveillance Video Using Machine Learning

Abdulrahman S. Alturki, Anwar H. Ibrahim, Feroz Shaik
2020 International journal of engineering research and technology  
In this proposed method the action identification is aided through the machine learning technology. Initially the frames of the subject under focus are segmented and modeled by the Gaussian modeling.  ...  The output of the proposed algorithm is analyzed and is said to be have the correct gesture identification with accuracy rate of 95.568%.  ...  The third model involves the incorporation of the variation of deep learning algorithms based on the machine learning model.  ... 
doi:10.37624/ijert/13.8.2020.1874-1879 fatcat:7jyvjbv5pnddbouqxzr3psrqka

Simultaneous Control of Artificial Limbs Based On Hybrid Extreme Learning Machine Algorithm
English

Daniel C, Aruna R
2015 International Journal of Innovative Research in Computer and Communication Engineering  
Pattern identification based algorithms with the purpose of make use of surface electromyography (EMG) signals calculated beginning residual muscles demonstrate huge assure as multi-DOF controllers.  ...  In this work, we proposed novel Hybrid Extreme Learning Machine (HELM) classification methods for the prediction of the limb movement and control them for individual through myoelectric signals.  ...  Establishment patterns of fundamental muscle synergies have been second-hand toward forecast the movement of numerous wrist DOFs [8] .  ... 
doi:10.15680/ijircce.2015.0306014 fatcat:exnebbag7fcipdyd6ctpzu5vie

A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users [article]

Xing Liang, Anastassia Angelopoulou, Epaminondas Kapetanios, Bencie Woll, Reda Al-batat, Tyron Woolfe
2020 arXiv   pre-print
In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features  ...  from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of  ...  machine learning approaches, and twelve used deep learning approaches.  ... 
arXiv:2010.00536v1 fatcat:xbv5qjetdfc6bnr5lukh4bpb5y

Automatic Functional Shoulder Task Identification and Sub-task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Chih-Ya Chang, Chia-Yeh Hsieh, Hsiang-Yun Huang, Yung-Tsan Wu, Liang-Cheng Chen, Chia-Tai Chan, Kai-Chun Liu
2020 Sensors  
The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately.  ...  To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder  ...  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  ... 
doi:10.3390/s21010106 pmid:33375341 pmcid:PMC7795360 fatcat:ucb4rgcsnnchhlxec7kixfripq

Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition

Shudi Wang, Li Huang, Du Jiang, Ying Sun, Guozhang Jiang, Jun Li, Cejing Zou, Hanwen Fan, Yuanmin Xie, Hegen Xiong, Baojia Chen
2022 Frontiers in Bioengineering and Biotechnology  
field of human-machine collaboration.  ...  The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition.  ...  FUNDING This work was supported by grants from the Nfromional Natural Science Foundation of China (Grant Nos.52075530, 51575407, 51505349, 51975324, 61733011, and 41906177)  ... 
doi:10.3389/fbioe.2022.909023 pmid:35747495 pmcid:PMC9209772 fatcat:kxqltma3p5grjec3oyqa62kmhu

CTNN: A Convolutional Tensor-Train Neural Network For Multi-task Brainprint Recognition

Xuanyu Jin, Jiajia Tang, Xianghao Kong, Yong Peng, Jianting Cao, Qibin Zhao, Wanzeng Kong
2020 IEEE transactions on neural systems and rehabilitation engineering  
Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task.  ...  This paper proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples.  ...  Subjects were required to watch movie clips, complete the hand movements imagination and hand grip movement 50 times respectively (25 for the left hand and 25 for the right hand).  ... 
doi:10.1109/tnsre.2020.3035786 pmid:33147145 fatcat:efumlali7fgvpdbbwtl6k42s6a

An Evidence-Based Intelligent Method for Upper-Limb Motor Assessment via a VR Training System on Stroke Rehabilitation

Si-Huei Lee, Jianjun Cui, Lizheng Liu, Mu-Chun Su, Lirong Zheng, Shih-Ching Yeh
2021 IEEE Access  
Finally, with the fusion of multi-model data, the accuracy rate of machine-learning assessment model was up to 92.72% which revealed its great potential for clinical use.  ...  Further, integrating multi-model data, such as motion trajectory, task performance and evaluation scales, machine-learning method was applied to develop evidence-based assessment models in order to evaluate  ...  In medical diagnosis, some researchers use machine learning to analyze medical images and detect the patient's status [44] ; some researchers use supervised machine learning algorithms to classify patient  ... 
doi:10.1109/access.2021.3075778 fatcat:6onfwutbjbbujkdjwvbeqvm6ke

Feature Extraction from sEMG of Forearm Muscles, Performance Analysis of Neural Networks and Support Vector Machines for Movement Classification

Luis Morales, Jaime Cepeda
2017 Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics  
Then, identification and classification of 5 types of movements are done, including open hand, closed hand, hand flexed inwards, out and relax position.  ...  The propose of this work is to extract different features from surface EMG signals of forearm muscles such as MAV, RMS, NZC, VAR, STD, PSD, and EOF's.  ...  Figure 5 : 5 Multi-channel sensor and its relation with the movements of the hand scheme.  ... 
doi:10.5220/0006429402540261 dblp:conf/icinco/MoralesC17 fatcat:jl34mkutu5g5lg573nq6kp6zt4

Reactive Rhythm Activities and Offline Classification of Imagined Speeds of Finger Movements

Yunfa Fu, Baolei Xu, Lili Pei, Hongyi Li
2011 2011 5th International Conference on Bioinformatics and Biomedical Engineering  
The Fisher discriminant analysis method (FDA), multi-layer perceptron neural network (MLP), and distinction sensitive learning vector quantization (DSLVQ) were applied in offline identification of imagined  ...  Keywords: Imagined movement speeds; Fisher discriminant analysis (FDA); Multi-layer perceptron (MLP); Distinction sensitive learning vector quantization (DSLVQ); Brain-controlled robot interface (BCRI)  ...  Fisher discriminant analysis, multi-layer perception (MLP), and distinction sensitive learning vector quantization (DSLVQ) were used to discriminate imagined speeds respectively.  ... 
doi:10.1109/icbbe.2011.5780247 fatcat:2judabposvdm5o7uzyhuzcze7i
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