Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal

Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function

Rekhi, N. S., Arora, A. S., Singh, S., & Singh, D. (2009) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2009.5163093 · Cited by 28

Design and implementation of a voice-controlled prosthetic hand

Asyalı, M. H., YILMAZ, M., Tokmakçı, M., Sedef, K., Aksebzeci, B. H., & MITTAL, R. (2011) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-0912-315 · Cited by 28

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution

Hayashi, H., Furui, A., Kurita, Y., & Tsuji, T. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2657121 · Cited by 28

Development of an sEMG sensor composed of two-layered conductive silicone with different carbon concentrations

Togo, S., Murai, Y., Jiang, Y., & Yokoi, H. (2019) · Scientific Reports · doi:10.1038/s41598-019-50112-4 · Cited by 28

Automatic discovery of resource-restricted Convolutional Neural Network topologies for myoelectric pattern recognition

Olsson, A. E., Björkman, A., & Antfolk, C. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103723 · Cited by 28

Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN

Ando, T., Robotics and Design for Innovative Healthcare, G. S. O. M., Watanabe, M., Nishimoto, K., Matsumoto, Y., Seki, M., Fujie, M. G., & Graduate School of Creative Science and Engineering, F. O. S. A. E. (2012) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2012.p0141 · Cited by 27

Deep neural network for EMG signal classification of wrist position: Preliminary results

Orjuela-Cañón, Á. D., Ruiz-Olaya, A. F., & Forero, L. (2017) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci.2017.8285706 · Cited by 27

EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies

Sarasola-Sanz, A., Irastorza-Landa, N., Shiman, F., López‐Larraz, E., Spüler, M., Birbaumer, N., & Ramos‐Murguialday, A. (2015) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2015.7281204 · Cited by 27

Hand-in-hand advances in biomedical engineering and sensorimotor restoration

Pisotta, I., Perruchoud, D., & Ionta, S. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.03.003 · Cited by 27

An advanced, low cost prosthetic arm

O'Neill, C. (2014) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2014.6985043 · Cited by 27

Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis

D’Accolti, D., Clemente, F., Mannini, A., Mastinu, E., Ortiz-Catalan, M., & Cipriani, C. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3235680 · Cited by 26

DESIGN AND DEVELOPMENT OF FIVE-FINGERED HANDS FOR A HUMANOID EMOTION EXPRESSION ROBOT

Roccella, S., Carrozza, M. C., Cappiello, G., Cabibihan, J., Laschi, C., Dario, P., TAKANOBU, H., Matsumoto, M., Miwa, H., Itoh, K., et al. (2007) · International Journal of Humanoid Robotics · doi:10.1142/s0219843607000996 · Cited by 26

A Practical Strategy for sEMG-Based Knee Joint Moment Estimation During Gait and Its Validation in Individuals With Cerebral Palsy

Kwon, S., Park, H., Stanley, C. J., Kim, J., Kim, J., & Damiano, D. L. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2187651 · Cited by 26

Soft wearable assistive robotics: exosuits and supernumerary limbs

Masia, L., Hussain, I., Xiloyannis, M., Pacchierotti, C., Cappello, L., Malvezzi, M., Spagnoletti, G., Antuvan, C. W., Dinh, B. K., Pozzi, M., et al. (2018) · Institution of Engineering and Technology eBooks · doi:10.1049/pbce108e_ch10 · Cited by 26

Fingers Movements Control System Based on Artificial Neural Network Model

Vonsevych, K. P., Goethel, M. F., Mrozowski, J., Awrejcewicz, J., & Bezuglyi, M. A. (2019) · Radioelectronics and Communications Systems · doi:10.3103/s0735272719010047 · Cited by 29

The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback

Tchimino, J., Marković, M., Dideriksen, J. L., & Dosen, S. S. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac07be · Cited by 25

A Learning‐Based Sensor Array for Untethered Soft Prosthetic Hand Aiming at Restoring Tactile Sensation

Xu, H., Rong, Y., Ren, J., Zhang, N., Zhao, Y., Yang, X., Zhu, Z., & Gu, G. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300221 · Cited by 26

Upper Limb Active Prosthetic systems—Overview

Castellini, C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814659-0.00019-9 · Cited by 25

Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network

Cheron, G. A., Cebolla, A. M., Bengoetxea, A., Leurs, F., & Dan, B. (2006) · Neuroscience Letters · doi:10.1016/j.neulet.2006.12.019 · Cited by 25

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

Said, S., Karar, A. S., Beyrouthy, T., Kork, S. A., & Naït-Ali, A. (2020) · Applied Sciences · doi:10.3390/app10196960 · Cited by 24

Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems

Liarokapis, M. V., Artemiadis, P., Katsiaris, P. T., & Kyriakopoulos, K. J. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290724 · Cited by 24

Forelimb EMG-based trigger to control an electronic spinal bridge to enable hindlimb stepping after a complete spinal cord lesion in rats

Gad, P. N., Woodbridge, J., Lavrov, I. A., Zhong, H., Roy, R. R., Sarrafzadeh, M., & Edgerton, V. R. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-38 · Cited by 24

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

Ribeiro, P. R. D. A., Brasil, F. L., Witkowski, M., Shiman, F., Cipriani, C., Vitiello, N., Carrozza, M. C., & Soekadar, S. R. (2013) · Advances in Human-Computer Interaction · doi:10.1155/2013/369425 · Cited by 24

Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM)

Malešević, N., Marković, D., Kanitz, G., Controzzi, M., Cipriani, C., & Antfolk, C. (2017) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2017.8009463 · Cited by 24

Adaptive Pattern Recognition of Myoelectric Signal towards Practical Multifunctional Prosthesis Control

He, J., Zhang, D., & Zhu, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33509-9_52 · Cited by 23

Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm

Kanitz, G., Antfolk, C., Cipriani, C., Sebelius, F., & Carrozza, M. C. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090465 · Cited by 23

Making sense of artificial hands

Chappell, P. H. (2010) · Journal of Medical Engineering & Technology · doi:10.3109/03091902.2010.526983 · Cited by 23

Shoulder Motion Intention Detection Through Myoelectric Pattern Recognition

Tigrini, A., Pettinari, L. A., Verdini, F., Fioretti, S., & Mengarelli, A. (2021) · IEEE Sensors Letters · doi:10.1109/lsens.2021.3100607 · Cited by 23

Task discrimination from myoelectric activity: A learning scheme for EMG-based interfaces

Liarokapis, M. V., Artemiadis, P., & Kyriakopoulos, K. J. (2013) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2013.6650366 · Cited by 23

Preliminary investigation of residual limb plantarflexion and dorsiflexion muscle activity during treadmill walking for trans-tibial amputees

Silver-Thorn, B., Current, T. A., & Kuhse, B. (2012) · Prosthetics and Orthotics International · doi:10.1177/0309364612443379 · Cited by 23

Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers

Kaufmann, P., Glette, K., Gruber, T., Platzner, M., Tørresen, J., & Sick, B. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2185845 · Cited by 23

EMG controlled multifunctional prosthetic hand: Preliminary clinical study and experimental demonstration

Zhang, D., Chen, X., Li, S., Hu, P., & Zhu, X. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980079 · Cited by 23

Considering Limb Impedance in the Design and Control of Prosthetic Devices

Perreault, E. J., Hargrove, L. J., Ludvig, D., Lee, H., & Sensinger, J. W. (2014) · Trends in augmentation of human performance · doi:10.1007/978-94-017-8932-5_3 · Cited by 23

Hand movement recognition based on singular value decomposition of surface EMG signal

Iqbal, O., Fattah, S. A., & Zahin, S. (2017) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc.2017.8289085 · Cited by 23

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition

Zhang, K., Badesa, F. J., Liu, Y., & Ferré, M. (2024) · Sensors · doi:10.3390/s24113631 · Cited by 23

Robust sEMG electrodes configuration for pattern recognition based prosthesis control

Fang, Y., & Liu, H. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974252 · Cited by 22

Using surface electromyography to predict single finger forces

Castellini, C., & Kõiva, R. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290294 · Cited by 22

Biomimetic grasp planning for cortical control of a robotic hand

Ciocarlie, M., Clanton, S. T., Spalding, M. C., & Allen, P. K. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4651179 · Cited by 22

Virtual reality upper limb model controlled by EMG signals

Muri, F., Carbajal, C., Echenique, A. M., Fernández, H., & López, N. M. (2013) · Journal of Physics Conference Series · doi:10.1088/1742-6596/477/1/012041 · Cited by 22

Towards an SEMG-based tele-operated robot for masticatory rehabilitation

Kalani, H., Moghimi, S., & Akbarzadeh, A. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.05.014 · Cited by 22

Design and Development of Real Time Bionic Hand Control Using EMG Signal

Praveen, L. S., Nagananda, S. N., & Shankapal, P. (2018) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct.2018.8482393 · Cited by 22

Characterization of tfLIFE Neural Response for the Control of a Cybernetic Hand

Citi, L., Carpaneto, J., Yoshida, K., Hoffmann, K. ‐., Koch, K. P., Dario, P., & Micera, S. (2006) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2006.1639134 · Cited by 22

Development of a control system for artificially rehabilitated limbs: a review

Bhuiyan, M. S. H., Choudhury, I. A., & Dahari, M. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0635-1 · Cited by 21

SEMG-based prediction of masticatory kinematics in rhythmic clenching movements

Kalani, H., Moghimi, S., & Akbarzadeh, A. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.04.003 · Cited by 21

Improving the performance of a neural-machine interface for prosthetic legs using adaptive pattern classifiers

Du, L., Zhang, F., He, H., & Huang, H. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609814 · Cited by 21

Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities

Rouillard, J., Duprès, A., Cabestaing, F., Leclercq, S., Bekaert, M.-H., Piau, C., Vannobel, J.-M., & Botte-Lecocq, C. (2015) · Procedia Manufacturing · doi:10.1016/j.promfg.2015.07.104 · Cited by 21

Biologically Inspired Robotics

Liu, Y., & Sun, D. B. (2012) · CRC Press eBooks · doi:10.1201/b11365 · Cited by 21

Classification of surface electromyographic signals for control of upper limb virtual prosthesis using time-domain features

Herle, S., Raica, P., Lazea, G., Robotin, R., Marcu, C., & Tamás, L. (2008) · IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) · doi:10.1109/aqtr.2008.4588902 · Cited by 20

A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands*

Schmalfuß, L., Hahne, J. M., Farina, D., Hewitt, M., Kogut, A., Doneit, W., Reischl, M., Rupp, R., & Liebetanz, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aad727 · Cited by 20

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

Sarasola-Sanz, A., Irastorza-Landa, N., López‐Larraz, E., Shiman, F., Spüler, M., Birbaumer, N., & Ramos‐Murguialday, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-34785-x · Cited by 21

Extraction of voluntary movement for an EMG controlled exoskeltal robot of tremor patients

Ando, T., Watanabe, M., & Fujie, M. G. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109249 · Cited by 20

An embedded controller for a 7-degree of freedom prosthetic arm

Tenore, F. V., Armiger, R. S., Vogelstein, R. J., Wenstrand, D. S., Harshbarger, S. D., & Englehart, K. B. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649121 · Cited by 20

Effects of muscle fatigue on the usability of a myoelectric human-computer interface

Barszap, A. G., Skavhaug, I.-M., & Joshi, S. S. (2016) · Human Movement Science · doi:10.1016/j.humov.2016.06.009 · Cited by 20

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile, C., & Gruppioni, E. (2023) · Prosthesis · doi:10.3390/prosthesis5040083 · Cited by 21

The Use of a Bone-Anchored Device as a Hard-Wired Conduit for Transmitting EMG Signals From Implanted Muscle Electrodes

Al-Ajam, Y., Lancashire, H. T., Pendegrass, C. J., Kang, N., Dowling, R., Taylor, S. J. G., & Blunn, G. W. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2241060 · Cited by 19

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

Olsson, A. E., Malešević, N., Björkman, A., & Antfolk, C. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00832-4 · Cited by 21

Radio frequency identification — An innovative solution to guide dexterous prosthetic hands

Trachtenberg, M., Singhal, G., Kaliki, R. R., Smith, R. J., & Thakor, N. V. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090948 · Cited by 19

Development of wireless EMG control system for rehabilitation devices

Champaty, B., Dubey, P., Sahoo, S., Ray, S. S., Pal, K., & Anis, A. (2014) · Annual International Conference on Emerging Research Areas and International Conference on Microelectronics, Communications and Renewable Energy · doi:10.1109/aicera.2014.6908260 · Cited by 19

Role of the Window Length for Myoelectric Pattern Recognition in Detecting User Intent of Motion

Tigrini, A., Scattolini, M., Mengarelli, A., Fioretti, S., Morettini, M., Burattini, L., & Verdini, F. (2022) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea54994.2022.9856454 · Cited by 18

Performance estimation of the lower limb exoskeleton for plantarflexion using surface electromyography (sEMG) signals

Park, J., Park, H., & Kim, J. (2017) · Journal of Biomechanical Science and Engineering · doi:10.1299/jbse.16-00595 · Cited by 18

Human-In-The-Loop Assessment of an Ultralight, Low-Cost Body Posture Tracking Device

Sierotowicz, M., Connan, M., & Castellini, C. (2020) · Sensors · doi:10.3390/s20030890 · Cited by 18

Reducing Motor Variability Enhances Myoelectric Control Robustness Across Untrained Limb Positions

Stuttaford, S. A., Dyson, M., Nazarpour, K., & Dupan, S. S. G. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3343621 · Cited by 17

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

Yan, Z., Wang, Z., & Ren, X. (2007) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.2007.a1246 · Cited by 17

Effect of dynamic change of arm position on myoelectric pattern recognition

Liu, J., Zhang, D., He, J., & Zhu, X. (2012) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2012.6491176 · Cited by 17

Using Different Features of Signal in EMG Signal Classification

Turgunov, A., Zohirov, K., Rustamov, S., & Muhtorov, B. (2020) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct50599.2020.9351392 · Cited by 17

Intrafascicular thin-film multichannel electrodes for sensory feedback: Evidences on a human amputee

Benvenuto, A., Raspopović, S., Hoffmann, K. P., Carpaneto, J., Cavallo, G., Pino, G. D., Guglielmelli, E., Rossini, L., Rossini, P. M., Tombini, M., et al. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626401 · Cited by 17

Using textile electrode EMG for prosthetic movement identification in transradial amputees

Zhang, H., Tian, L., Zhang, L., & Li, G. (2013) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2013.6575510 · Cited by 17

Real time virtual prosthetic hand controlled using EMG signals

Fraiwan, L., Awwad, M., Mahdawi, M., & Jamous, S. (2011) · Middle East Conference on Biomedical Engineering · doi:10.1109/mecbme.2011.5752106 · Cited by 17

Surface Electromyography: Its Role and Potential in the Development of Exoskeleton (Review)

Rukina, N. N., Kuznetsov, A. N., Борзиков, В. В., Komkova, O. V., & Belova, A. N. (2016) · Современные технологии в медицине · doi:10.17691/stm2016.8.2.15 · Cited by 17

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

Mayor, J. J. V., Costa, R. M., Frizera, A., & Bastos-Filho, T. (2017) · Research on Biomedical Engineering · doi:10.1590/2446-4740.08516 · Cited by 17

Tremor frequency based filter to extract voluntary movement of patients with essential tremor

Matsumoto, Y., Seki, M., Ando, T., Kobayashi, Y., Iijima, H., Nagaoka, M., & Fujie, M. G. (2012) · 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290905 · Cited by 16

Electromyogram-based hand gesture recognition robust to various arm postures

Rhee, K., & Shin, H. (2018) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147718790751 · Cited by 16

Neural Network-Based Muscle Torque Estimation Using Mechanomyography During Electrically-Evoked Knee Extension and Standing in Spinal Cord Injury

Dzulkifli, M. A., Hamzaid, N. A., Davis, G. M., & Hasnan, N. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00050 · Cited by 16

Hybrid control combined with a voluntary biosignal to control a prosthetic hand

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Neural control in prostheses and exoskeletons

Sharbafi, M. A., Naseri, A., Andre Seyfarth, A., & Grimmer, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817450-0.00007-9 · Cited by 16

A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm

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Postural Synergies and Neural Network for Autonomous Grasping: A Tool for Dextrous Prosthetic and Robotic Hands

Ficuciello, F., Palli, G., Melchiorri, C., & Siciliano, B. (2013) · Biosystems & biorobotics · doi:10.1007/978-3-642-34546-3_76 · Cited by 16

Two-channel real-time EMG control of a dexterous hand prosthesis

Matrone, G. C., Cipriani, C., Carrozza, M. C., & Magenes, G. (2011) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2011.5910608 · Cited by 16

A Hybrid Classification Model for EMG Signals Using Grey Wolf Optimizer and SVMs

Elhariri, E., El-Bendary, N., & Hassanien, A. E. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-26690-9_27 · Cited by 16

Toward a bio-inspired rehabilitation aid: sEMG-CPG approach for online generation of jaw trajectories for a chewing robot

Kalani, H., Moghimi, S., & Akbarzadeh, A. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.02.022 · Cited by 16

Fractal Analysis of Surface Electromyography (EMG) Signal for Identify Hand Movements Using Critical Exponent Analysis

Phinyomark, A., Phothisonothai, M., Suklaead, P., Phukpattaranont, P., & Limsakul, C. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22191-0_62 · Cited by 15

Speed–accuracy trade-offs in myocontrol

Fimbel, E. J., Lemay, M., & Arguin, M. (2006) · Human Movement Science · doi:10.1016/j.humov.2005.12.001 · Cited by 15

Performance of various EMG features in identifying ARM movements for control of multifunctional prostheses

Liu, X., Zhou, R., Yang, L., & Li, G. (2009) · IEEE Youth Conference on Information, Computing and Telecommunication (YC-ICT) · doi:10.1109/ycict.2009.5382366 · Cited by 15

Analysis of EMG signal using wavelet coefficients for upper limb function

Rekhi, N. S., Singh, H., Arora, A. S., & Rekhi, A. K. (2009) · IEEE International Conference on Computer Science and Information Technology · doi:10.1109/iccsit.2009.5234929 · Cited by 15

Longitudinal intrafascicular electrodes in collection and analysis of sensory signals of the peripheral nerve in a feline model

Li, L.-J., Zhang, J., Zhang, F., Lineaweaver, W. C., Chen, T.-Y., & Chen, Z.-W. (2005) · Microsurgery · doi:10.1002/micr.20159 · Cited by 14

Real-time comparison of conventional direct control and pattern recognition myoelectric control in a two-dimensional Fitts' law style test

Wurth, S. M., & Hargrove, L. J. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610329 · Cited by 14

Design and evaluation of a 3-degree-of-freedom upper limb rehabilitation exoskeleton robot

Gao, B., Ma, H., Guo, S., Xu, H., & Shu, Y. (2017) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2017.8015942 · Cited by 14

Simultaneous estimation of 2-DOF wrist movements based on constrained non-negative matrix factorization and Hadamard product

Yang, D., Li, J., Zhang, X., & Liu, H. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101729 · Cited by 14

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Leone, F., Gentile, C., Cordella, F., Gruppioni, E., Guglielmelli, E., & Zollo, L. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-00982-z · Cited by 14

Brain-muscle-computer interface using a single surface electromyographic signal: Initial results

Joshi, S. S., Wexler, A. S., Perez-Maldonado, C., & Vernon, S. (2011) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2011.5910557 · Cited by 14

Learning to modulate the partial powers of a single sEMG power spectrum through a novel human–computer interface

Skavhaug, I.-M., Lyons, K. R., Nemchuk, A., Muroff, S. D., & Joshi, S. S. (2016) · Human Movement Science · doi:10.1016/j.humov.2015.12.003 · Cited by 14

Finger motion estimation based on frequency conversion of EMG signals and image recognition using convolutional neural network

Asai, K., & Takase, N. (2017) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas.2017.8204206 · Cited by 14

Linear vs. non-linear mapping of peak power using surface EMG features during dynamic fatiguing contractions

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Critical Exponent Analysis Applied to Surface EMG Signals for Gesture Recognition

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