The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal
Abstract
The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contray, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none ofthese methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“the formal scheme for the acquisition and analysis of EMG signals for the control of prosthetic devices is composed of several modules (Zecca et al., 2002) : (1) signal conditioning, pre-processing, and detection of onset movement (Micera et al., 1999) ; (2) feature extraction; (3) dimensionality reduction; (4) pattern recognition; and (5) off-line and on-line learning.”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Zecca et al., 2002
In the citing paper
“Among the different approaches for restoring the link between the external world and the nervous system, the use of implantable intrafascicular PNS interfaces is of great potential, as confirmed in previous experiments in humans [6,7].”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [7]
In the citing paper
“In fact, after appropriate control in experimental models to test their biocompatibility and efficacy [18], LIFEs have been recently used to control artificial devices [6,7,22-25] showing good results during short-term trials with human amputees.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [7]
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Cited by
Sorted by citations
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875
Feature reduction and selection for EMG signal classification
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.102 · Cited by 1,605
Myoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
Restoring natural sensory feedback in real-time bidirectional hand prostheses
Raspopovic, S., Capogrosso, M., Petrini, F. M., Bonizzato, M., Rigosa, J., Di Pino, G., Carpaneto, J., Controzzi, M., Boretius, T., Fernandez, E., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3006820 · Cited by 1,001
Self-citationNavarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845
Self-citationEMG feature evaluation for improving myoelectric pattern recognition robustness
Phinyomark, A., Quaine, F., Charbonnier, S., Servière, C., Tarpin-Bernard, F., & Laurillau, Y. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.02.023 · Cited by 638
Study of stability of time-domain features for electromyographic pattern recognition
Tkach, D. C., Huang, H., & Kuiken, T. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-21 · Cited by 581
Myoelectric forearm prostheses: State of the art from a user-centered perspective
Peerdeman, B., Boere, D., Witteveen, H. J. B., `tVeld, R. H. I., Hermens, H. J., Stramigioli, S., Rietman, H. S., Veltink, P. H., & Misra, S. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.08.0161 · Cited by 467
On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction
Cipriani, C., Zaccone, F., Micera, S., & Carrozza, M. C. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.910708 · Cited by 450
Self-citationHand Posture Subspaces for Dexterous Robotic Grasping
Ciocarlie, M., & Allen, P. K. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909105606 · Cited by 406
Control of Hand Prostheses Using Peripheral Information
Micera, S., Carpaneto, J., & Raspopović, S. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2085429 · Cited by 405
Self-citationMethodological aspects of SEMG recordings for force estimation – A tutorial and review
Staudenmann, D., Roeleveld, K., Stegeman, D. F., & van Dieën, J. H. (2009) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2009.08.005 · Cited by 379
Decoding of Individuated Finger Movements Using Surface Electromyography
Tenore, F. V., Ramos‐Murguialday, A., Fahmy, A., Acharya, S., Etienne‐Cummings, R., & Thakor, N. V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2005485 · Cited by 344
Design of a cybernetic hand for perception and action
Carrozza, M. C., Cappiello, G., Micera, S., Edin, B. B., Beccai, L., & Cipriani, C. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0124-2 · Cited by 340
Self-citationAl-Timemy, A. H., Khushaba, R. N., Bugmann, G., & Escudero, J. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2445634 · Cited by 335
Electromyographic models to assess muscle fatigue
González‐Izal, M., Malanda, A., Gorostiaga, E. M., & Izquierdo, M. (2012) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2012.02.019 · Cited by 311
The Usefulness of Mean and Median Frequencies in Electromyography Analysis
Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., & Limsakul, C. (2012) · InTech eBooks · doi:10.5772/50639 · Cited by 305
Current state of digital signal processing in myoelectric interfaces and related applications
Hakonen, M., Piitulainen, H., & Visala, A. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.02.009 · Cited by 305
Characterization of a Benchmark Database for Myoelectric Movement Classification
Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Hager, A.-G. M., Dériaz, O., Castellini, C., Müller, H., & Barbara Caputo, B. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2328495 · Cited by 300
Young, A. J., Hargrove, L. J., & Kuiken, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2159216 · Cited by 287
Muceli, S., & Farina, D. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2178039 · Cited by 285
Building the Ninapro database: A resource for the biorobotics community
Atzori, M., Gijsberts, A., Heynen, S., Hager, A.-G. M., Dériaz, O., van der Smagt, P., Castellini, C., Barbara Caputo, B., & Müller, H. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290287 · Cited by 272
Prosthetic Myoelectric Control Strategies: A Clinical Perspective
Roche, A. D., Rehbaum, H., Farina, D., & Aszmann, O. C. (2014) · Current Surgery Reports · doi:10.1007/s40137-013-0044-8 · Cited by 271
Young, A. J., Hargrove, L. J., & Kuiken, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2177662 · Cited by 251
Sensinger, J. W., Lock, B. A., & Kuiken, T. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2023282 · Cited by 241
A review of the key technologies for sEMG-based human-robot interaction systems
Li, K., Zhang, J., Wang, L., Zhang, M., Li, J., & Bao, S. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102074 · Cited by 236
Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control
Amsüss, S., Goebel, P. M., Jiang, N., Graimann, B., Paredes, L. P., & Farina, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2296274 · Cited by 203
Ngeo, J., Tamei, T., & Shibata, T. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-122 · Cited by 199
Samuel, O. W., Asogbon, M. G., Geng, Y., Al-Timemy, A. H., Pirbhulal, S., Ji, N., Chen, S., Fang, P., & Li, G. (2019) · IEEE Access · doi:10.1109/access.2019.2891350 · Cited by 185
Cesqui, B., Tropea, P., Micera, S., & Krebs, H. I. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-75 · Cited by 173
Self-citationA Compliant Ionic Adhesive Electrode with Ultralow Bioelectronic Impedance
Pan, L., Cai, P., Mei, L., Cheng, Y., Zeng, Y., Wang, M., Wang, T., Jiang, Y., Ji, B., Li, D., et al. (2020) · Advanced Materials · doi:10.1002/adma.202003723 · Cited by 171
Real-time intelligent pattern recognition algorithm for surface EMG signals
Khezri, M., & Jahed, M. (2007) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-6-45 · Cited by 165
Fine detection of grasp force and posture by amputees via surface electromyography
Castellini, C., Gruppioni, E., Davalli, A., & Sandini, G. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.08.008 · Cited by 159
Bu, N., Okamoto, M., & Tsuji, T. (2009) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2019782 · Cited by 159
Multi-subject/daily-life activity EMG-based control of mechanical hands
Castellini, C., Fiorilla, A. E., & Sandini, G. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-41 · Cited by 155
Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks
Trigili, E., Grazi, L., Crea, S., Accogli, A., Carpaneto, J., Micera, S., Vitiello, N., & Panarese, A. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0512-1 · Cited by 163
Self-citationTowards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals
Tenore, F. V., Ramos‐Murguialday, A., Fahmy, A., Acharya, S., Etienne‐Cummings, R., & Thakor, N. V. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353752 · Cited by 154
A Neuro–Fuzzy Inference System for sEMG-Based Identification of Hand Motion Commands
Khezri, M., & Jahed, M. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2053334 · Cited by 153
Real-Time Classification of Hand Motions Using Ultrasound Imaging of Forearm Muscles
Akhlaghi, N., Baker, C. A., Lahlou, M., Zafar, H., Murthy, K. G., Rangwala, H., Košecká, J., Joiner, W. M., Pancrazio, J. J., & Sikdar, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2498124 · Cited by 153
Non-invasive control interfaces for intention detection in active movement-assistive devices
Lobo-Prat, J., Kooren, P. N., Stienen, A. H. A., Herder, J. L., Koopman, H. F. J. M., & Veltink, P. H. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-168 · Cited by 150
Feature extraction of the first difference of EMG time series for EMG pattern recognition
Phinyomark, A., Quaine, F., Charbonnier, S., Servière, C., Tarpin-Bernard, F., & Laurillau, Y. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.06.013 · Cited by 144
Targeted Muscle Reinnervation for the Upper and Lower Extremity
Kuiken, T. A., Barlow, A. K., Hargrove, L. J., & Dumanian, G. A. (2017) · Techniques in Orthopaedics · doi:10.1097/bto.0000000000000194 · Cited by 143
Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation
Dosen, S. S., Cipriani, C., Kostic, M. D., Controzzi, M., Carrozza, M. C., & Popović, D. B. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-42 · Cited by 143
Self-citationA Switching Regime Model for the EMG-Based Control of a Robot Arm
Artemiadis, P., & Kyriakopoulos, K. J. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2045120 · Cited by 142
Application of EMG signals for controlling exoskeleton robots
Fleischer, C., Wege, A., Kondak, K., & Hommel, G. (2006) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt.2006.063 · Cited by 138
Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies
Farina, D., & Negro, F. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2183586 · Cited by 137
Evaluation of surface EMG-based recognition algorithms for decoding hand movements
Abbaspour, S., Lindén, M., GholamHosseini, H., Naber, A., & Ortiz-Catalan, M. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02073-z · Cited by 137
Hussain, I., Salvietti, G., Spagnoletti, G., & Prattichizzo, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2530793 · Cited by 137
A synergy-based hand control is encoded in human motor cortical areas
Leo, A., Handjaras, G., Bianchi, M., Marino, H., Gabiccini, M., Guidi, A., Scilingo, E. P., Pietrini, P., Bicchi, A., Santello, M., et al. (2016) · eLife · doi:10.7554/elife.13420 · Cited by 137
Brain-machine interfaces for rehabilitation in stroke: A review
López‐Larraz, E., Sarasola-Sanz, A., Irastorza-Landa, N., Birbaumer, N., & Ramos‐Murguialday, A. (2018) · Neurorehabilitation · doi:10.3233/nre-172394 · Cited by 138
Control of prosthetic hands via the peripheral nervous system
Ciancio, A. L., Cordella, F., Barone, R., Romeo, R. A., Bellingegni, A. D., Sacchetti, R., Davalli, A., Di Pino, G., Ranieri, F., Di Lazzaro, V., et al. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00116 · Cited by 131
Review on electromyography signal acquisition and processing
Gohel, V., & Mehendale, N. (2020) · Biophysical Reviews · doi:10.1007/s12551-020-00770-w · Cited by 129
Lee, S. W., Landers, K. A., & Park, H. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2298362 · Cited by 128
Principal components analysis based control of a multi-dof underactuated prosthetic hand
Matrone, G. C., Cipriani, C., Secco, E. L., Magenes, G., & Carrozza, M. C. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-16 · Cited by 127
Self-citationNLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation
Bellingegni, A. D., Gruppioni, E., Colazzo, G., Davalli, A., Sacchetti, R., Guglielmelli, E., & Zollo, L. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0290-6 · Cited by 127
Micera, S., Navarro, X., Carpaneto, J., Citi, L., Tonet, O., Rossini, P. M., Carrozza, M. C., Hoffmann, K. P., Vivo, M., Yoshida, K., et al. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2006207 · Cited by 120
Self-citationOn the challenge of classifying 52 hand movements from surface electromyography
Kuzborskij, I., Gijsberts, A., & Barbara Caputo, B. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347099 · Cited by 119
Bio-signal based control in assistive robots: a survey
Rechy-Ramirez, E. J., & Hu, H. (2015) · Digital Communications and Networks · doi:10.1016/j.dcan.2015.02.004 · Cited by 118
User adaptation in Myoelectric Man-Machine Interfaces
Hahne, J. M., Marković, M., & Farina, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-04255-x · Cited by 118
Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand
Wang, N., Lao, K., & Zhang, X. (2016) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(16)60377-3 · Cited by 117
Liu, J., & Zhou, P. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2218832 · Cited by 116
Smith, R. J., Tenore, F. V., Huberdeau, D. M., Etienne‐Cummings, R., & Thakor, N. V. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649124 · Cited by 114
A Method for the Control of Multigrasp Myoelectric Prosthetic Hands
Dalley, S. A., Varol, H. A., & Goldfarb, M. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2175488 · Cited by 113
Fluctuating emg signals: Investigating long-term effects of pattern matching algorithms
Kaufmann, P. M., Englehart, K. B., & Platzner, M. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627288 · Cited by 113
Active upper limb prostheses: a review on current state and upcoming breakthroughs
Marinelli, A., Boccardo, N., Tessari, F., Domenico, D. D., Caserta, G., Canepa, M., Gini, G. C., Barresi, G., Laffranchi, M., Michieli, L. D., et al. (2022) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/acac57 · Cited by 123
Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control
Scheme, E., Lock, B. A., Hargrove, L. J., Hill, W., Kuruganti, U., & Englehart, K. B. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2247421 · Cited by 109
A Comprehensive Study on EMG Feature Extraction and Classifiers
Spiewak, C. (2018) · Open Access Journal of Biomedical Engineering and Biosciences · doi:10.32474/oajbeb.2018.01.000104 · Cited by 103
Neural machine interfaces for controlling multifunctional powered upper-limb prostheses
Ohnishi, K., Weir, R. F. F., & Kuiken, T. (2006) · Expert Review of Medical Devices · doi:10.1586/17434440.4.1.43 · Cited by 103
Fractal analysis features for weak and single-channel upper-limb EMG signals
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.039 · Cited by 103
Micera, S., Rossini, P. M., Rigosa, J., Citi, L., Carpaneto, J., Raspopovic, S., Tombini, M., Cipriani, C., Assenza, G., Carrozza, M. C., et al. (2011) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-8-53 · Cited by 100
Self-citationMicera, S., Citi, L., Rigosa, J., Carpaneto, J., Raspopović, S., Pino, G. D., Rossini, L., Yoshida, K., Denaro, L., Dario, P., et al. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2038726 · Cited by 102
Self-citationGA-based Feature Subset Selection for Myoelectric Classification
Oskoei, M. A., & Hu, H. (2006) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2006.340145 · Cited by 100
Riillo, F., Quitadamo, L. R., Cavrini, F., Gruppioni, E., Pinto, C. A., Pastò, N. C., Sbernini, L., Albero, L., & Saggio, G. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.07.007 · Cited by 97
Wurth, S. M., & Hargrove, L. J. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-91 · Cited by 96
Electromyography sensor based control for a hand exoskeleton
Wege, A., & Zimmermann, A. (2007) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2007.4522381 · Cited by 96
Technological Advances in Prosthesis Design and Rehabilitation Following Upper Extremity Limb Loss
Bates, T. J., Fergason, J. R., & Pierrie, S. N. (2020) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-020-09656-6 · Cited by 95
The Body-Machine Interface: A New Perspective on an Old Theme
Casadio, M., Ranganathan, R., & Mussa-Ivaldi, F. A. (2012) · Journal of Motor Behavior · doi:10.1080/00222895.2012.700968 · Cited by 100
An adaptation strategy of using LDA classifier for EMG pattern recognition
Zhang, H., Zhao, Y., Yao, F., Xu, L., Shang, P., & Li, G. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610488 · Cited by 92
Design of a robust EMG sensing interface for pattern classification
Huang, H., Zhang, F., Sun, Y. L., & He, H. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/5/056005 · Cited by 90
Khezri, M., Firoozabadi, M., & Sharafat, A. R. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.07.006 · Cited by 89
Model adaptation with least-squares SVM for adaptive hand prosthetics
Orabona, F., Castellini, C., Barbara Caputo, B., Fiorilla, A. E., & Sandini, G. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152247 · Cited by 89
Improving Control of Dexterous Hand Prostheses Using Adaptive Learning
Tommasi, T., Orabona, F., Castellini, C., & Barbara Caputo, B. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2226386 · Cited by 87
Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor
Tang, X., Liu, Y., Lv, C., & Sun, D. B. (2012) · Sensors · doi:10.3390/s120201130 · Cited by 87
Feature extraction and classification for EMG signals using linear discriminant analysis
Negi, S., Kumar, Y., & Mishra, V. M. (2016) · International Conference on Advances in Computing, Communication & Automation (ICACCA) · doi:10.1109/icaccaf.2016.7748960 · Cited by 85
Hussain, I., Spagnoletti, G., Salvietti, G., & Prattichizzo, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917712433 · Cited by 86
Kumar, D., Jelfs, B., Sui, X., & Arjunan, S. P. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101588 · Cited by 85
Real time ECG artifact removal for myoelectric prosthesis control
Zhou, P., Lock, B. A., & Kuiken, T. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/4/006 · Cited by 82
Huang, H., Xie, H.-B., Guo, J., & Chen, H. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.10.004 · Cited by 82
Raspopovic, S., Carpaneto, J., Udina, E., Navarro, X., & Micera, S. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-17 · Cited by 81
Self-citationCarrozza, M. C., Cappiello, G., Stellin, G., Zaccone, F., Vecchi, F., Micera, S., & Dario, P. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570515 · Cited by 81
Self-citationA quantitative taxonomy of human hand grasps
Stival, F., Michieletto, S., Cognolato, M., Pagello, E., Müller, H., & Atzori, M. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0488-x · Cited by 81
Hand and Wrist Movement Control of Myoelectric Prosthesis Based on Synergy
Ma, J., Thakor, N. V., & Matsuno, F. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2358634 · Cited by 80
Quantification and solutions of arm movements effect on sEMG pattern recognition
Liu, J., Zhang, D., Sheng, X., & Zhu, X. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.05.001 · Cited by 80
CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors
Zhang, T., Ali, A. E., Wang, C., Hanjalić, A., & Cesar, P. S. (2020) · Sensors · doi:10.3390/s21010052 · Cited by 79
Survey on Mapping Human Hand Motion to Robotic Hands for Teleoperation
Li, R., Wang, H., & Liu, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3057992 · Cited by 82
Hybrid Bionic Systems for the Replacement of Hand Function
Micera, S., Carrozza, M. C., Beccai, L., Vecchi, F., & Dario, P. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.881294 · Cited by 75
Self-citationKhushaba, R. N., Al-Jumaily, A. A., & Al-Ani, A. K. (2007) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2007.4392044 · Cited by 73
Dario, P., Carrozza, M. C., Guglielmelli, E., Laschi, C., Menciassi, A., Micera, S., & Vecchi, F. (2005) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2005.1458320 · Cited by 72
Self-citationArtificial neural network EMG classifier for functional hand grasp movements prediction
Gandolla, M., Ferrante, S., Ferrigno, G., Baldassini, D., Molteni, F., Guanziroli, E., Cottini, M. C., Seneci, C., & Pedrocchi, A. (2016) · Journal of International Medical Research · doi:10.1177/0300060516656689 · Cited by 72
Evidence of muscle synergies during human grasping
Castellini, C., & van der Smagt, P. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0548-4 · Cited by 71
Metadata
- Authors
- Zecca, M., Micera, S., Carrozza, M. C., & Dario, P.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- Venue
- Critical Reviews? in Biomedical Engineering
- Publisher
- Begell House
- Published
- 2002
- Volume
- 30
- Issue
- 4-6
- Pages
- 459-485
- ISSN
- 0278-940X (ISSN-L)
- OpenAlex
- W4242470988
- Cited by
- 606
- References
- 0
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 16 · 2023: 23 · 2022: 19 · 2021: 18 · 2020: 26 · 2019: 37 · 2018: 37 · 2017: 41 · 2016: 31 · 2015: 31 · 2014: 57 · 2013: 44 · 2012: 38
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- prosthetic hand, myoelectric control, EMG signal processing, degrees of freedom, grasping, sensory feedback
- MeSH terms
- Algorithms, Artificial Limbs, Electromyography, Feedback, Hand, Hand Injuries, Humans, Pattern Recognition, Automated, Prosthesis Design, Quality Control, Signal Processing, Computer-Assisted, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
