Log in

Relevant bibliographies by topics / EMG control / Journal articles

To see the other types of publications on this topic, follow the link: EMG control.

Journal articles on the topic 'EMG control'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'EMG control.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Adila Ferdiansyah, Faizal, Prawito Prajitno, and Sastra Kusuma Wijaya. "EEG-EMG based bio-robotics elbow orthotics control." Journal of Physics: Conference Series 1528 (April 2020): 012033. http://dx.doi.org/10.1088/1742-6596/1528/1/012033.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Awari, Rohan, Prof R. B. Kakkeri, and Radha Chande. "EMG Based Robot Control." IJIREEICE 7, no. 5 (May 30, 2019): 14–16. http://dx.doi.org/10.17148/ijireeice.2019.7504.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Abdullah, Saad, Muhammad A. Khan, Mauro Serpelloni, and Emilio Sardini. "Hybrid EEG-EMG Based Brain Computer Interface (BCI) System For Real-Time Robotic Arm Control." Advanced Materials Letters 10, no. 1 (December 10, 2018): 35–40. http://dx.doi.org/10.5185/amlett.2019.2171.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Bortel, Radoslav, and Pavel Sovka. "EEG–EMG coherence enhancement." Signal Processing 86, no. 7 (July 2006): 1737–51. http://dx.doi.org/10.1016/j.sigpro.2005.09.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Gordleeva, S. Yu, S. A. Lobov, V. I. Mironov, I. A. Kastalskiy, M. V. Lukoyanov, N. P. Krilova, I. V. Mukhina, A. Ya Kaplan, and V. B. Kazantsev. "DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES." Science and Innovations in Medicine 1, no. 3 (September 15, 2016): 77–82. http://dx.doi.org/10.35693/2500-1388-2016-0-3-77-82.

Full text

Abstract:

Aim - to develop a hardware-software complex with combined command-proportional control of robotic devices based on electromyography (EMG) and electroencephalography (EEG) signals. Materials and methods. EMG and EEG signals are recorded using our original units. The system also supports a number of commercial EEG and EMG recording systems, such as NVX52 (MCS ltd, Russia), DELSYS Trigno (Delsys Inc, USA), MYO Thalmic (Thalmic Labs, Canada). Raw signals undergo preprocessing and feature extraction. Then features are fed to classifiers. The interpretation unit controls robotic devices on the base of classified EEG- and EMG-patterns and muscle effort estimation. The number of controlled devices includes mobile robot LEGO NXT Mindstorms (LEGO, Denmark), humanoid robot NAO (Aldebaran, France) and exoskeleton Ilia Muromets (UNN, Russia). Results. We have developed and tested an interface combining command and proportional control based on EMG signals. We have determined the parameters providing optimal characteristics of classification accuracy of EMG patterns, as well as the speed and accuracy of proportional control. Also we have developed and tested a BCI interface based on motor imagined patterns. Both EMG and EEG interfaces are included into hardware and software system. The system combines outputs of the interfaces and sends commands to a robotic device. Conclusion. We have developed and approved the hardware-software system on the basis of the combined command-proportional EMG and EEG control of external robotic devices.

APA, Harvard, Vancouver, ISO, and other styles

6

Danna-Dos-Santos, Alessander, Tjeerd W. Boonstra, Adriana M. Degani, Vinicius S. Cardoso, Alessandra T. Magalhaes, Luis Mochizuki, and Charles T. Leonard. "Multi-muscle control during bipedal stance: an EMG–EMG analysis approach." Experimental Brain Research 232, no. 1 (October 9, 2013): 75–87. http://dx.doi.org/10.1007/s00221-013-3721-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Wheeler, K. R., M. H. Chang, and K. H. Knuth. "Gesture-based control and EMG decomposition." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 4 (July 2006): 503–14. http://dx.doi.org/10.1109/tsmcc.2006.875418.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Chan, F. H. Y., Yong-Sheng Yang, F. K. Lam, Yuan-Ting Zhang, and P. A. Parker. "Fuzzy EMG classification for prosthesis control." IEEE Transactions on Rehabilitation Engineering 8, no. 3 (2000): 305–11. http://dx.doi.org/10.1109/86.867872.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Block, Susan, Mark Onslow, Rachael Roberts, and Samantha White. "Control of stuttering with EMG feedback." Advances in Speech Language Pathology 6, no. 2 (June 2004): 100–106. http://dx.doi.org/10.1080/14417040410001708521.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Glaros, Alan G., and Karen Hanson. "EMG biofeedback and discriminative muscle control." Biofeedback and Self-Regulation 15, no. 2 (June 1990): 135–43. http://dx.doi.org/10.1007/bf00999144.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Harver, Andrew, Joyce Segreto, and Harry Kotses. "EMG stability as a biofeedback control." Biofeedback and Self-Regulation 17, no. 2 (June 1992): 159–64. http://dx.doi.org/10.1007/bf01000107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Sherwood, David E. "Electromyographic Control of Movement Time in a Rapid Aiming Movement." Perceptual and Motor Skills 107, no. 2 (October 2008): 353–64. http://dx.doi.org/10.2466/pms.107.2.353-364.

Full text

Abstract:

One of the major issues to emerge from research on human-limb movement is the manner in which the central nervous system regulates electromyographic (EMG) activity to produce movements that differ in duration and distance. Different models of control predict different relations between EMC characteristics and movement kinematics, particularly with regard to the role of EMC burst duration and movement time. However, models have been evaluated with means averaged over individuals and across large numbers of practice trials. The goal of this study was to assess how well individual subjects' data conform to the predictions of the control models. Participants ( n = 4) performed an elbow flexion and extension task over 45° in movement times between 90 and 260 msec. EMG amplitude and EMG burst duration from the right elbow flexors were correlated with movement time for each individual. As expected, movement time was positively correlated with EMG burst duration and negatively correlated with EMG amplitude, with wider ranges in the EMG burst duration–movement time correlations across participants. Data from all participants supported predictions of the impulse-timing control model, but the slopes of the studied relations varied across participants.

APA, Harvard, Vancouver, ISO, and other styles

13

ISHII, Chiharu. "Control of an Electric Wheelchair Based on EMG, EOG and EEG." Journal of the Japan Society for Precision Engineering 83, no. 11 (2017): 1006–9. http://dx.doi.org/10.2493/jjspe.83.1006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

GOPURA, R. A. R. C., and Kazuo KIGUCHI. "1P1-E13 EMG-Based Control of a 6DOF Upper-Limb Exoskeleton Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _1P1—E13_1—_1P1—E13_3. http://dx.doi.org/10.1299/jsmermd.2009._1p1-e13_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Artemiadis, Panagiotis K., and Kostas J. Kyriakopoulos. "An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features." IEEE Transactions on Information Technology in Biomedicine 14, no. 3 (May 2010): 582–88. http://dx.doi.org/10.1109/titb.2010.2040832.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Kageyama, Yoshiyuki, Kiyoyuki Yamazaki, and Kiyotaka Hoshiai. "Robot Arm Control by Selectively Generated EMG." Journal of Robotics and Mechatronics 5, no. 3 (June 20, 1993): 302–5. http://dx.doi.org/10.20965/jrm.1993.p0302.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

R., Aishwarya, Prabhu M., Sumithra G., and Anusiya M. "FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL." ICTACT Journal on Soft Computing 03, no. 02 (January 1, 2013): 472–77. http://dx.doi.org/10.21917/ijsc.2013.0071.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Raghavan, Anjali, and Sunny Joseph. "EMG analysis and control of artificial arm." International Journal on Cybernetics & Informatics 5, no. 2 (April 30, 2016): 317–27. http://dx.doi.org/10.5121/ijci.2016.5234.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Oweis, Rami J., Remal Rihani, and Afnan Alkhawaja. "ANN-based EMG classification for myoelectric control." International Journal of Medical Engineering and Informatics 6, no. 4 (2014): 365. http://dx.doi.org/10.1504/ijmei.2014.065442.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lenzi, T., S. M. M. De Rossi, N. Vitiello, and M. C. Carrozza. "Intention-Based EMG Control for Powered Exoskeletons." IEEE Transactions on Biomedical Engineering 59, no. 8 (August 2012): 2180–90. http://dx.doi.org/10.1109/tbme.2012.2198821.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Marquez-Figueroa, Sandra, Yuriy S. Shmaliy, and Oscar Ibarra-Manzano. "Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (August 5, 2021): 106–12. http://dx.doi.org/10.37394/23208.2021.18.12.

Full text

Abstract:

Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.

APA, Harvard, Vancouver, ISO, and other styles

22

DaSalla, C., J. Kim, and Y. Koike. "Robot Control Using Electromyography (EMG) Signals of the Wrist." Applied Bionics and Biomechanics 2, no. 2 (2005): 97–102. http://dx.doi.org/10.1155/2005/952754.

Full text

Abstract:

The aim of this paper is to design a human–interface system, using EMG signals elicited by various wrist movements, to control a robot. EMG signals are normalized and based on joint torque. A three-layer neural network is used to estimate posture of the wrist and forearm from EMG signals. After training the neural network and obtaining appropriate weights, the subject was able to control the robot in real time using wrist and forearm movements.

APA, Harvard, Vancouver, ISO, and other styles

23

Ide, Hideto, Ryosuke Hosaka, and Masao Ohtsuka. "Fuzzy Control of Robot Hand Based on EMG." Journal of Robotics and Mechatronics 3, no. 5 (October 20, 1991): 435–36. http://dx.doi.org/10.20965/jrm.1991.p0435.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Kumar,, Mr C. Sathish, Divya Sree H, Kumudhavalli S, Swathy G, and Thejaswini J. "Designing And Control of Prosthetic Leg Using EMG." International Journal of Research in Advent Technology 7, no. 3 (April 10, 2019): 1503–4. http://dx.doi.org/10.32622/ijrat.732019136.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

VeerSinghRana, Aditya, and Ridhi Aggarwal. "2-d Robotic Arm Control using EMG Signal." International Journal of Computer Applications 72, no. 14 (June 26, 2013): 18–22. http://dx.doi.org/10.5120/12562-8944.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Küçük, Serdar, and Umut Mayetin. "Wirless control of mobile robot with EMG signals." Pamukkale University Journal of Engineering Sciences 23, no. 5 (2017): 497–503. http://dx.doi.org/10.5505/pajes.2016.79735.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Reddy, Narender P., and Rajeev Unnikrishnan. "EMG Interfaces for VR and Telematic Control Applications." IFAC Proceedings Volumes 34, no. 9 (July 2001): 443–46. http://dx.doi.org/10.1016/s1474-6670(17)41746-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Neilson, Peter D. "EMG bursts, sampling, and strategy in movement control." Behavioral and Brain Sciences 12, no. 2 (June 1989): 228–29. http://dx.doi.org/10.1017/s0140525x00048457.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Giuffrida, J. P., and P. E. Crago. "Reciprocal EMG control of elbow extension by FES." IEEE Transactions on Neural Systems and Rehabilitation Engineering 9, no. 4 (2001): 338–45. http://dx.doi.org/10.1109/7333.1000113.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Bruun, E., and E. U. Haxthausen. "Current conveyor based EMG amplifier with shutdown control." Electronics Letters 27, no. 23 (1991): 2172. http://dx.doi.org/10.1049/el:19911344.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Chen, Minjie, and Honghai Liu. "Robot arm control method using forearm EMG signals." MATEC Web of Conferences 309 (2020): 04007. http://dx.doi.org/10.1051/matecconf/202030904007.

Full text

Abstract:

With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals are finally transmitted to the robotic arm, so that people can teleoperate the robotic arm via sEMG signals in real time. Reduce the number of channels to lower the cost while ensuring a high and usable recognition rate. Experiments were performed by collecting EMG signals from the forearm surface of eight healthy volunteers. The experimental results show that the system’s overall gesture recognition accuracy rate can reach up to 90%, and the system responds fast, laying a good foundation for manipulating artificial limbs in the future.

APA, Harvard, Vancouver, ISO, and other styles

32

Drapała, Jarosław, Krzysztof Brzostowski, Agnieszka Szpala, and Alicja Rutkowska-Kucharska. "Two stage EMG onset detection method." Archives of Control Sciences 22, no. 4 (December 1, 2012): 427–40. http://dx.doi.org/10.2478/v10170-011-0033-z.

Full text

Abstract:

Detection of the moment when a muscle begins to activate on the basis of EMG signal is important task for a number of biomechanical studies. In order to provide high accuracy of EMG onset detection, we developed novel method, that give results similar to that obtained by an expert. By means of this method, EMG is processed in two stages. The first stage gives rough estimation of EMG onset, whereas the second stage performs local, precise searching. The method was applied to support signal processing in biomechanical study concerning effect of body position on EMG activity and peak muscle torque stabilizing spinal column under static conditions

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Xu, Xiaoting Ren, Xiaoping Gao, Xiang Chen, and Ping Zhou. "Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control." Entropy 18, no. 4 (March 30, 2016): 106. http://dx.doi.org/10.3390/e18040106.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

ZECCA, Massimiliano, Jacopo CARPANETO, Silvestro MICERA, M. Chiara CARROZZA, Paolo DARIO, Kazuko ITOH, and Atsuo TAKANISHI. "2P2-A05 Evolutionary Design of a Fuzzy Classifier for EMG-based Control : Control of a Multi-DoFs Underactuated Hand Prosthesis." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _2P2—A05_1—_2P2—A05_4. http://dx.doi.org/10.1299/jsmermd.2006._2p2-a05_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Yang, Geng Huang, Fei Fei Wang, Shi Gang Cui, Li Zhao, Qing Guo Meng, and Hong Da Chen. "Implementation of Human-Machine Interface Based on Electroencephalogram and Electromyography." Applied Mechanics and Materials 63-64 (June 2011): 385–89. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.385.

Full text

Abstract:

The Electfoencephalogram (EEG) and electromyography (EMG) sampled from skin surface are the primary information to mirror the idea of human being. The human-machine interface based on EEG and EMG is used to control machine such as a robot. It is a new taste to apply this type of interface to some special condition such as an astronaut controlling the outside robot in a space ship. Digital signal processor (DSP) is used as sample EEG and EMG in the device. The feature of signal is extract by algorithm running in DSP to control the machine. The speech recognition based on fixed Chinese words is included in the device. Many tests proved that the developed device is capable to control the robot for key operation on a panel with high reliability.

APA, Harvard, Vancouver, ISO, and other styles

36

FUKUDA, Osamu, Nan BU, and Toshio TSUJI. "Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals." Transactions of the Society of Instrument and Control Engineers 40, no. 11 (2004): 1124–31. http://dx.doi.org/10.9746/sicetr1965.40.1124.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Ahn, Bummo, Seong Young Ko, and Gi-Hun Yang. "Compliance Control of Slave Manipulator Using EMG Signal for Telemanipulation." Applied Sciences 10, no. 4 (February 20, 2020): 1431. http://dx.doi.org/10.3390/app10041431.

Full text

Abstract:

Telemanipulation systems have been widely utilized in industrial, surgical, educational, and even military fields. One of the important issues is that when a robot interacts with environment or objects, it can damage the robot itself or the objects due to hard contact. To address this problem, we propose a novel compliance control of a slave robot using the electromyography (EMG) signal, which is measured by the sensors attached onto the master operator’s arm. The EMG signal is used since it is easy to process and it provides humans with an intuitive capability to perform the operational work. Furthermore, it has been proved that the EMG signal is useful in the control of the stiffness of the slave robot. This research identifies the muscle that is the best suitable to a precision-grip operation, and a series of experiments were performed. A compliance control algorithm with a variable stiffness of a slave robot is proposed, where the stiffness is changed based on the EMG signal, and it is confirmed by a series of experiments using a two-channel force/position teleoperation architecture.

APA, Harvard, Vancouver, ISO, and other styles

38

Enders, Hendrik, and Benno M. Nigg. "Measuring human locomotor control using EMG and EEG: Current knowledge, limitations and future considerations." European Journal of Sport Science 16, no. 4 (August 4, 2015): 416–26. http://dx.doi.org/10.1080/17461391.2015.1068869.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Sagawa, Koichi, and Orie Kimura. "Robot manipulator control using EMG generated from a face." International Journal of Applied Electromagnetics and Mechanics 36, no. 1-2 (May 31, 2011): 85–93. http://dx.doi.org/10.3233/jae-2011-1347.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Jerath, Himani, and Kavala Kotesh Phani Rohith. "EMG Sensor based Wheel Chair Control and Safety System." Research Journal of Pharmacy and Technology 12, no. 6 (2019): 2730. http://dx.doi.org/10.5958/0974-360x.2019.00457.8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Rahmatillah, Akif, Osmalina Nur Rahma, Muhammad Amin, Septian Indra Wicaksana, Khusnul Ain, and Riries Rulaningtyas. "Post-Stroke Rehabilitation Exosceleton Movement Control using EMG Signal." International Journal on Advanced Science, Engineering and Information Technology 8, no. 2 (April 3, 2018): 616. http://dx.doi.org/10.18517/ijaseit.8.2.4960.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Ryu, Kwang-Ryol. "Surface EMG Network Analysis and Robotic Arm Control Implementation." Journal of information and communication convergence engineering 9, no. 6 (December 31, 2011): 743–46. http://dx.doi.org/10.6109/ijice.2011.9.6.743.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Ching Sung, Yen Ju Chiang, Chih Chung Chang, Jia He Lin, Wei Jie Lin, and Siang Jyun Jheng. "Implement a Control Mechanical Legs System by EMG Signals." Advanced Materials Research 219-220 (March 2011): 1633–38. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1633.

Full text

Abstract:

As an auxiliary facility to assist a disabled patient to perform a normal walk, the crutch or the wheelchair is considered to provide an essential mobility as a mechanical facility. However, these facilities can not provide a basic function for these patients to sustain a standing position except a mechanical prosthesis. To control the activity of the prosthesis, a muscle membrane potential produced via an arm swing during the period of a walk is used as a control signal. In this paper, we adopt a dual ADC interface on a STM3210E-LK board to extract two kinds of muscle membrane potentials on a single arm. These two potential signals can be used to control the activities of the mechanical prosthesis. In the experiment we use these two potential signals to control the activities of a humanoid robot to simulate the activities of the mechanical prosthesis.

APA, Harvard, Vancouver, ISO, and other styles

44

MORITA, Satoshi, Katsunari SHIBATA, Xin Zhi ZHENG, and Koji ITO. "EMG Prosthetic Hand Control based on the Torque Estimation." Proceedings of the JSME annual meeting 2000.1 (2000): 375–76. http://dx.doi.org/10.1299/jsmemecjo.2000.1.0_375.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Gradetsky, Valery, Ivan Ermolov, Maxim Knyazkov, and Artem Sukhanov. "Generalized approach to bilateral control for EMG driven exoskeleton." MATEC Web of Conferences 113 (2017): 02003. http://dx.doi.org/10.1051/matecconf/201711302003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

TSUJIUCHI, Nobutaka, Takayuki KOIZUMI, and Mitsuhiro YONEDA. "121 Motion Classification using EMG Signals and Robot Control." Proceedings of Conference of Kansai Branch 2004.79 (2004): _1–39_—_1–40_. http://dx.doi.org/10.1299/jsmekansai.2004.79._1-39_.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Manal, Kurt. "Real-Time Control of an EMG-Driven Virtual Arm." Medicine & Science in Sports & Exercise 36, Supplement (May 2004): S1. http://dx.doi.org/10.1097/00005768-200405001-00005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Ameri, Ali, Mohammad Ali Akhaee, Erik Scheme, and Kevin Englehart. "Regression convolutional neural network for improved simultaneous EMG control." Journal of Neural Engineering 16, no. 3 (April 16, 2019): 036015. http://dx.doi.org/10.1088/1741-2552/ab0e2e.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

DaSalla, C., J. Kim, and Y. Koike. "Robot control using electromyography (EMG) signals of the wrist." Applied Bionics and Biomechanics 2, no. 2 (February 2005): 97–102. http://dx.doi.org/10.1533/abbi.2004.0054.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Nishikawa, Daisuke, Wenwei Yu, Hiroshi Yokoi, and Yukinori Kakazu. "On-line learning method for EMG prosthetic hand control." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, no. 10 (2001): 35–46. http://dx.doi.org/10.1002/ecjc.1040.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!