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.
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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]
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Mattar, E. A. (2014) · UKSim International Conference on Computer Modelling and Simulation (UKSim) · doi:10.1109/uksim.2014.122 · Cited by 2
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Ramkumar, S., Rema, D., Devi, T. A., Elavarasi, K., Selvaganapathi, T., & Gokila, S. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426249 · Cited by 1
Yazawa, I., Yoneda, S., & Furui, A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11251792 · Cited by 1
Mahfoodh, W. A., & Ali, D. M. (2026) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd69655.2026.11558926 · Cited by 1
Bio-Inspired Mechatronics and Control Interfaces
Artemiadis, P., & Kyriakopoulos, K. J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-1452-1_23 · Cited by 1
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Muceli, S. (2013) · doi:10.13052/rp-9788793102361 · Cited by 1
Kim, M., & Kim, K. (2013) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2013.6703885 · Cited by 1
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Lalitha, A., & Thakor, N. V. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.403-408.3973 · Cited by 1
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Heger, M., Stavdahl, Ø., & Torp, H. G. (2014) · IEEE International Ultrasonics Symposium · doi:10.1109/ultsym.2014.0469 · Cited by 1
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Ando, T., Okamoto, J., & Fujie, M. G. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354540 · Cited by 1
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Yoshikawa, M., Mita, T., Mikawa, M., & Tanaka, K. (2010) · The Japanese Journal of Ergonomics · doi:10.5100/jje.46.197 · Cited by 1
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Khamis, A., Chen, C.-H., & Naidu, D. S. (2016) · UKACC International Conference on Control · doi:10.1109/control.2016.7737638 · Cited by 1
Nguyen, D. Q., Pham, T. C., & Quan, T. (2020) · Science & Technology Development Journal - Engineering and Technology · doi:10.32508/stdjet.v3isi1.536 · Cited by 1
Spinal Interfacing via Muscle Recordings for Neuroprosthesis Control
Muceli, S., & Farina, D. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_29 · Cited by 1
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Vonsevych, K. P. (2023) · Studies in systems, decision and control · doi:10.1007/978-3-031-44347-3_6 · Cited by 1
Owen, M., Au, C. K., August, L. R. J., Guan, B., & Liarokapis, M. V. (2024) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med61351.2024.10566242 · Cited by 1
Chaitra, S., Nayana, B. R., Pushphavati, T. P., & Vinayak, Y. G. (2024) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset63057.2024.10895354 · Cited by 1
Chapter 1 Emg‐Based and Gaze‐Tracking‐Based Man–Machine Interfaces
Carpi, F., & Rossi, D. D. (2009) · International review of neurobiology · doi:10.1016/s0074-7742(09)86001-7 · Cited by 1
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Hordacre, B. G., Ridding, M. C., & Bradnam, L. V. (2015) · Disability and Rehabilitation · doi:10.3109/09638288.2015.1103790 · Cited by 1
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Song, Z., Wang, Z., & Guo, S. (2013) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/iccme.2013.6548288 · Cited by 1
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Ruiz-Olaya, A. F., & López-Delis, A. (2015) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-4666-7373-1.ch001 · Cited by 1
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Lu, H., Zhang, H., Lin, M., & Wang, R. (2016) · International Conference on Digital Home (ICDH) · doi:10.1109/icdh.2016.049 · Cited by 1
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Chen, Y., & Zhang, Y.-W. (2017) · International Journal of Control and Automation · doi:10.14257/ijca.2017.10.9.06 · Cited by 1
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Shivam, P., Kakoty, N. M., Malarvili, M. B., & Widiyanti, P. (2019) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc47129.2019.9042432 · Cited by 1
Kovalev, Y., Bergaliyev, T., & Sakhno, S. V. (2020) · International Conference on Engineering and Telecommunication (EnT) · doi:10.1109/ent50437.2020.9431317 · Cited by 1
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Stuttaford, S. A., Dupan, S. S. G., Nazarpour, K., & Dyson, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629609 · Cited by 1
Radha, H. M., Hassan, A. K. A., & Al-Timemy, A. H. (2023) · Mathematical Modelling and Engineering Problems · doi:10.18280/mmep.100316 · Cited by 1
EMG based design and evaluation of Micro Macro Neural Network for rollover support trunk orthosis
Ando, T., Okamoto, J., Takahashi, M., & Fujie, M. G. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509868 · Cited by 1
Development of a Micro-Macro Neural Network to recognize rollover movement
Ando, T., Okamoto, J., & Fujie, M. G. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650393 · Cited by 1
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Hartmann, C. (2017) · doi:10.53846/goediss-6432 · Cited by 1
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Morrow, D. A. (2011) · doi:10.37099/mtu.dc.etds/388 · Cited by 1
Chen, C.-H., & Naidu, D. S. (2017) · Wiley eBooks · doi:10.1002/9781119273622.ch7 · Cited by 1
Valdunciel, A. P. (2022) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.71544 · Cited by 1
Investigations on upper limb prosthesis control with an active elbow
Mérad, M. (2017) · theses.fr (ABES) · doi:10.70675/49868946zf54fz40ccza88bzda97441889ed · Cited by 1
Said, S. (2020) · theses.fr (ABES) · doi:10.70675/dd9c45ddza2aaz46bfzaff8z04f45452183d · Cited by 1
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Tarniţă, D., & Berceanu, C. (2013) · Mechanisms and machine science · doi:10.1007/978-3-319-01592-7_16
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Cheein, F. A., di, F., Marcos, J., & Carelli, R. (2010) · InTech eBooks · doi:10.5772/9316
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Choi, C., & Kim, J. (2010) · InTech eBooks · doi:10.5772/8136
Muri, F., Carbajal, C., Pérez, E., Fernández, H., Echenique, A. M., & López, N. (2014) · Revista Ingeniería Biomédica · doi:10.24050/19099762.n14.2013.475
Ghazanfari, S., Nasrabadi, A. M., & Firoozabadi, M. (2012) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2012.6519658
Tachibana, H., Suzuki, T. J., & Mabuchi, K. (2010) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.130.254
Kobayashi, Y., Fukayama, O., Suzuki, T., & Mabuchi, K. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627504
Augmenting neuroprosthetic hand control through evaluation of a bioacoustic interface
Mace, M., Subbich, S., Naeem, A. A., & Vaidyanathan, R. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696547
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Díaz, S. A. A., Vazquez, J. R. M., Gonzalez, J. L. V., & Sánchez, A. D. (2009) · International Conference on Electronics, Communications, and Computers · doi:10.1109/conielecomp.2009.55
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Wołczowski, A. R., Szecówka, P. M., Góra, J., & Kurzynski, M. W. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-00969-8_77
Control Scheme for Independent Thumb Motion in a Myoelectrically Controlled Hand Prosthesis
Vaidya, N., Buchholz, B., Mahd, M., & Crugnola, A. (2011) · JPO Journal of Prosthetics and Orthotics · doi:10.1097/jpo.0b013e31822fcd1a
Tongue Movement Estimation Based on Suprahyoid Muscle Activity
SASAKI, M. (2014) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-4666-6090-8.ch012
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Carpaneto, J., Citi, L., Raspopović, S., Rigosa, J., & Micera, S. (2013) · Wiley eBooks · doi:10.1002/9781118628522.ch16
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Suarez, Á., Osorio, A., López-Gutiérrez, R., Salazar, S., & Lozano, R. (2016) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc.2016.7790761
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Lopez, J. C. B., & Vilela-Malabanan, C. M. (2017) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2017.8269561
Alanís, A. Y., Arana‐Daniel, N., & López-Franco, C. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813788-8.00002-0
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Chatterjee, A. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-8585-7_21
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Vonsevych, K. P., Гетцель, М. Ф., Мрозовски, Е., Аврейцевич, Я., & Bezuglyi, M. A. (2019) · Известия высших учебных заведений Радиоэлектроника · doi:10.20535/s0021347019010047
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Piña Díaz, A. J. (2019) · MOJ Applied Bionics and Biomechanics · doi:10.15406/mojabb.2019.03.00114
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Khan, M. N. H., & Fattah, S. A. (2019) · IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) · doi:10.1109/becithcon48839.2019.9063196
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Fermeiro, J., Moreira, F., Pombo, J., Calado, R., & Mariano, S. J. P. S. (2020) · KnE Engineering · doi:10.18502/keg.v5i6.7101
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Yin, Z. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202127101030
Oskouei, A. H., Emamian, A., & Carman, A. B. (2021) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2021.07.033
Chen, C.-H., & Naidu, D. S. (2017) · Wiley eBooks · doi:10.1002/9781119273622.ch1
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Rehbaum, H. (2014) · doi:10.53846/goediss-4483
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Çelik, M. S., Tepe, C., & Eminoğlu, İ. (2022) · Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.21597/jist.1082384
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Mattioli, F. E. R. (2012) · doi:10.14393/ufu.di.2012.22
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Scattolini, M., Tigrini, A., Verdini, F., Fioretti, S., & Mengarelli, A. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00229
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Tyagi, T., Pandey, A., & Singh, K. R. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183549
Facultad de Medicina, U. A. D. E. D. M., Guadarrama, G. E. M., Osornio, R. I. M. , Facultad de Medicina, U. A. D. E. D. M., Iturbe, F. X. R., Facultad de Medicina, U. A. D. E. D. M., Ramírez, C. I. L., Facultad de Medicina, U. A. D. E. D. M., Carrillo, A. C. P. , Facultad de Medicina, U. A. D. E. D. M., et al. (2018) · Memorias del Congreso Nacional de Ingeniería Biomédica · doi:10.24254/cnib.18.30
Framework for the Classification of Facial Emotions Using Soft Computing Techniques
Maity, S., & Veer, K. (2024) · Current Signal Transduction Therapy · doi:10.2174/0115743624273918240102060402
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Anisha, P. R., Kaza, V. S., Badam, R., & Vulapula, S. R. (2023) · International Conference on Sensing Technology · doi:10.1109/icst59744.2023.10460858
Electromyography Sensor Controlled Bionic Hand for Disabled Persons
Panse, Y. G. T. (2024) · Journal of Electrical Systems · doi:10.52783/jes.1747
THIKESAVAN, D., Alphin, M. S., Sarate, S. G., RAMRAJI, K., & ADHIL, S. M. (2023) · ROMANIAN BIOTECHNOLOGICAL LETTERS · doi:10.25083/rbl/28.1/3819.3834
Classification of Hand Gestures Using sEMG Signals and TensorFlow Lite for Microcontrollers
Alves, E., Maia, H., & Arf, O. (2023) · Anais do Congresso Brasileiro Interdisciplinar em Ciência e Tecnologia. · doi:10.29327/1298891.4-431
On the evaluation of statistical and mathematical solutions in EMG analysis
Şimşek, B., Şavklıyıldız, A., Süzen, E., Polat, Ö., & Çolak, Ö. H. (2024) · AIP conference proceedings · doi:10.1063/5.0210141
Athikesavan, D., Alphin, M. S., Sarate, S. G., & Aathil, S. M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106642
RetractedShafiei, S. B., Shadpour, S., & Toussi, M. S. (2025) · Journal of Robotic Surgery · doi:10.1007/s11701-025-02505-z
Karabulut, D., & Doğru, S. C. (2025) · Gazi University Journal of Science Part A Engineering and Innovation · doi:10.54287/gujsa.1700850
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Takele, A. T., & Shiferaw, D. (2025) · IEEE Access · doi:10.1109/access.2025.3589294
Treatment and beyond of chronic disease with AI
Alanís, A. Y., Mendez-Palos, E., Sánchez, O. D., & Magaña, R. D. S. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332856-5.00015-0
Prosthetic Hand Driven by Electromyographic Signals
Gokul, S., Alamelu Alias Rajasree., S., Kowsalya., M., Kalaimathi, B., Chockalingam., S., & Giridharan, G. V. (2025) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds65426.2025.11167076
Sinthia, P., Mary, J. A. J., & Priyanka, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7289-9_20
Guan, B., de Godoy, R. V., Dwivedi, A., & Liarokapis, M. V. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252975
Guan, B., Kobayashi, M., de Godoy, R. V., Owen, M., & Liarokapis, M. V. (2025) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe66822.2025.00095
Exploring Heterogeneous Federated Learning for Improved Hand Gesture Recognition
Shahjahan, S., Paragliola, G., Research, M. N., & Syed, M. H. (2024) · International Conference on Innovative Computing (ICIC) · doi:10.1109/icic63915.2024.11116571
Alanís, A. Y., Hernandez, H. M., Valdivia-G, A., & Sanchez, O. D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10150-1_29
Neuromuscular Fatigue During Heavy Resistance Drop Set Training to Failure
Jakobsen, M. D., Sundstrup, E., & Andersen, L. L. (2026) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000005105
Auricular Muscle- controlled Navigation for Powered WheelchairsPreprint
Nowak, A., Fleming, J., & Zecca, M. (2026) · medRxiv · doi:10.64898/2026.02.28.26347311
Self-citationSmart control of a soft robotic hand prosthesis
Rubiano, A. (2016) · theses.fr (ABES) · doi:10.70675/7495eb03z2d1az4cf3zaa13z9edf9dcf33fe
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
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