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.
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The formal scheme for EMG acquisition and analysis for prosthetic control includes signal conditioning, pre-processing, onset detection, feature extraction, dimensionality reduction, pattern recognition, and off-line and on-line learning.
“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
The use of implantable intrafascicular PNS interfaces is of great potential, as confirmed in previous experiments in humans.
“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]
LIFEs have been recently used to control artificial devices showing good results during short-term trials with human amputees.
“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]
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Cipriani, C., Zaccone, F., Micera, S., & Carrozza, M. C. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.910708 · Cited by 450 · see citing works
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 · see citing works
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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 · see citing works
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 · see citing works
Design of a cybernetic hand for perception and action
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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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Young, A. J., Hargrove, L. J., & Kuiken, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2159216 · Cited by 287 · see citing works
Muceli, S., & Farina, D. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2178039 · Cited by 285 · see citing works
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 · see citing works
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Roche, A. D., Rehbaum, H., Farina, D., & Aszmann, O. C. (2014) · Current Surgery Reports · doi:10.1007/s40137-013-0044-8 · Cited by 262 · see citing works
Young, A. J., Hargrove, L. J., & Kuiken, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2177662 · Cited by 251 · see citing works
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 240 · see citing works
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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 · see citing works
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 · see citing works
Ngeo, J., Tamei, T., & Shibata, T. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-122 · Cited by 199 · see citing works
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 · see citing works
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 · see citing works
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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 169 · see citing works
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Khezri, M., & Jahed, M. (2007) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-6-45 · Cited by 165 · see citing works
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Castellini, C., Gruppioni, E., Davalli, A., & Sandini, G. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.08.008 · Cited by 159 · see citing works
Bu, N., Okamoto, M., & Tsuji, T. (2009) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2019782 · Cited by 159 · see citing works
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Castellini, C., Fiorilla, A. E., & Sandini, G. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-41 · Cited by 155 · see citing works
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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 155 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Hussain, I., Salvietti, G., Spagnoletti, G., & Prattichizzo, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2530793 · Cited by 134 · see citing works
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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 133 · see citing works
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López‐Larraz, E., Sarasola-Sanz, A., Irastorza-Landa, N., Birbaumer, N., & Ramos‐Murguialday, A. (2018) · Neurorehabilitation · doi:10.3233/nre-172394 · Cited by 132 · see citing works
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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 · see citing works
Review on electromyography signal acquisition and processing
Gohel, V., & Mehendale, N. (2020) · Biophysical Reviews · doi:10.1007/s12551-020-00770-w · Cited by 129 · see citing works
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 · see citing works
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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 · see citing works
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 · see citing works
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 · see citing works
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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 · see citing works
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Rechy-Ramirez, E. J., & Hu, H. (2015) · Digital Communications and Networks · doi:10.1016/j.dcan.2015.02.004 · Cited by 118 · see citing works
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 · see citing works
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 · see citing works
Liu, J., & Zhou, P. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2218832 · Cited by 116 · see citing works
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 · see citing works
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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 · see citing works
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 · see citing works
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 110 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 100 · see citing works
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 · see citing works
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 · see citing works
Wurth, S. M., & Hargrove, L. J. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-91 · Cited by 96 · see citing works
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 · see citing works
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 · see citing works
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 93 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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Tommasi, T., Orabona, F., Castellini, C., & Barbara Caputo, B. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2226386 · Cited by 87 · see citing works
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 · see citing works
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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 · see citing works
Hussain, I., Spagnoletti, G., Salvietti, G., & Prattichizzo, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917712433 · Cited by 83 · see citing works
Kumar, D., Jelfs, B., Sui, X., & Arjunan, S. P. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101588 · Cited by 82 · see citing works
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Zhou, P., Lock, B. A., & Kuiken, T. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/4/006 · Cited by 82 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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Hybrid Bionic Systems for the Replacement of Hand Function
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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 · see citing works
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 · see citing works
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Evidence of muscle synergies during human grasping
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- 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
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