Assessment of myoelectric controller performance and kinematic behavior of a novel soft synergy-inspired robotic hand for prosthetic applications
Open accessAbstract
Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Pattern recognition control of myoelectric prostheses is getting popularity due to better classification of EMG signals using various methods.
“Pattern recognition control of myoelectric prostheses is also getting popularity due to its better classification of EMG signals (user intentions) using various methods, i.e., linear discriminant analysis classifier, tacit learning system, deep learning with convolutional neural network (Adewuyi et al., 2016; Atzori et al., 2016; Fani et al., 2016; Oyama et al., 2016), etc.”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Fani et al., 2016
The Introduction and Discussion (page unavailable) describe machine-learning methods that map EMG patterns to prosthesis control commands. They do not establish that pattern-recognition control is growing in popularity because of better classification, or support the listed classifier methods. The study itself tests three EMG-to-movement mapping algorithms, not those classifiers. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-008-0001-3 · Cited by 3,193 · see citing works
Postural Hand Synergies for Tool Use
Santello, M., Flanders, M., & Soechting, J. F. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-23-10105.1998 · Cited by 1,215 · see citing works
Upper limb prosthesis use and abandonment
Biddiss, E. A., & Chau, T. T. (2007) · Prosthetics and Orthotics International · doi:10.1080/03093640600994581 · Cited by 1,123 · see citing works
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 · see citing works
Adaptive synergies for the design and control of the Pisa/IIT SoftHand
Catalano, M. G., Grioli, G., Farnioli, E., Serio, A., Piazza, C., & Bicchi, A. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364913518998 · Cited by 673 · see citing works
Birglen, L., Laliberté, T., & Gosselin, C. (2008) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-540-77459-4 · Cited by 572 · cited 1× · see citing works
“Another important feature of the SH design is that it is underactuated (Birglen et al., 2007).”
BackgroundOrtiz-Catalan, M., Håkansson, B., & Brånemark, R. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3008933 · Cited by 484 · see citing works
Jiang, N., Englehart, K. B., & Parker, P. A. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2007967 · Cited by 453 · see citing works
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 · 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
How anthropomorphism affects empathy toward robots
Riek, L. D., Rabinowitch, T.-C., Chakrabarti, B., & Robinson, P. (2009) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1514095.1514158 · Cited by 320 · see citing works
Santello, M., Bianchi, M., Gabiccini, M., Ricciardi, E., Salvietti, G., Prattichizzo, D., Ernst, M. O., Moscatelli, A., Jörntell, H., Kappers, A. M. L., et al. (2016) · Physics of Life Reviews · doi:10.1016/j.plrev.2016.02.001 · Cited by 308 · see citing works
Ison, M. R., & Artemiadis, P. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/051001 · Cited by 225 · see citing works
Modelling natural and artificial hands with synergies
Bicchi, A., Gabiccini, M., & Santello, M. (2011) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2011.0152 · Cited by 221 · see citing works
Castellini, C., Artemiadis, P., Wininger, M., Ajoudani, A., Alimusaj, M., Bicchi, A., Barbara Caputo, B., Craelius, W., Dosen, S. S., Englehart, K. B., et al. (2014) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2014.00022 · Cited by 221 · see citing works
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 · see citing works
Aszmann, O. C., Roche, A. D., Salminger, S., Paternostro-Sluga, T., Herceg, M., Sturma, A., Hofer, C., & Farina, D. (2015) · The Lancet · doi:10.1016/s0140-6736(14)61776-1 · Cited by 152 · see citing works
Solnik, S., DeVita, P., Rider, P. M., Long, B. L., & Hortobágyi, T. (2008) · PubMed · Cited by 135 · see citing works
Culture on the Brink: Ideologies of Technology
Druckrey, T., Aronowitz, S., Schiller, H. I., Woodward, K. M., Schirmacher, W., Scarry, E., Marks, J. H., Lewontin, R. C., Treichler, P. A., Chapman, G. T., et al. (1994) · Medical Entomology and Zoology · Cited by 134 · see citing works
THE USE OF MYO-ELECTRIC CURRENTS IN THE OPERATION OF PROSTHESES
Battye, C. K., Nightingale, A., & Whillis, J. (1955) · Journal of Bone and Joint Surgery - British Volume · doi:10.1302/0301-620x.37b3.506 · Cited by 130 · see citing works
Exploring Teleimpedance and Tactile Feedback for Intuitive Control of the Pisa/IIT SoftHand
Ajoudani, A., Godfrey, S. B., Bianchi, M., Catalano, M. G., Grioli, G., Tsagarakis, N. G., & Bicchi, A. (2014) · IEEE Transactions on Haptics · doi:10.1109/toh.2014.2309142 · Cited by 128 · see citing works
Adaptive synergies for a humanoid robot hand
Catalano, M. G., Grioli, G., Serio, A., Farnioli, E., Piazza, C., & Bicchi, A. (2012) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2012.6651492 · Cited by 122 · see citing works
The Sixth-Finger: A modular extra-finger to enhance human hand capabilities
Prattichizzo, D., Malvezzi, M., Hussain, I., & Salvietti, G. (2014) · IEEE RO-MAN · doi:10.1109/roman.2014.6926382 · Cited by 116 · see citing works
González, D., & Castellini, C. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00017 · Cited by 96 · see citing works
Ison, M. R., & Artemiadis, P. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2395731 · Cited by 88 · see citing works
A field evaluation of arm prostheses for unilateral amputees
van Lunteren, A., van Lunteren-Gerritsen, G. H. M., STASSEN, H. G., & Zuithoff, M. J. (1983) · Prosthetics and Orthotics International · doi:10.3109/03093648309166586 · Cited by 83 · see citing works
Murray, C. D. (2007) · Psychoprosthetics · doi:10.1007/978-1-84628-980-4_9 · Cited by 79 · see citing works
Bonilla, M., Farnioli, E., Piazza, C., Catalano, M. G., Grioli, G., Garabini, M., Gabiccini, M., & Bicchi, A. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041421 · Cited by 76 · see citing works
Integrating human observer inferences into robot motion planning
Dragan, A. D., & Srinivasa, S. S. (2014) · Autonomous Robots · doi:10.1007/s10514-014-9408-x · Cited by 73 · see citing works
Dexterity augmentation on a synergistic hand: The Pisa/IIT SoftHand+
Santina, C. D., Grioli, G., Catalano, M. G., Brando, A., & Bicchi, A. (2015) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2015.7363595 · Cited by 59 · see citing works
Tele-impedance: Towards transferring human impedance regulation skills to robots
Ajoudani, A., Tsagarakis, N. G., & Bicchi, A. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224904 · Cited by 57 · see citing works
Gabiccini, M., Stillfried, G., Marino, H., & Bianchi, M. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696890 · Cited by 51 · see citing works
A synergy-driven approach to a myoelectric hand
Godfrey, S. B., Ajoudani, A., Catalano, M. G., Grioli, G., & Bicchi, A. (2013) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2013.6650377 · Cited by 51 · see citing works
Teleimpedance control of a synergy-driven anthropomorphic hand
Ajoudani, A., Godfrey, S. B., Catalano, M. G., Grioli, G., Tsagarakis, N. G., & Bicchi, A. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696620 · Cited by 34 · see citing works
Does an Artificial Limb Become Part of the User?
Fraser, C. (1984) · British Journal of Occupational Therapy · doi:10.1177/030802268404700207 · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Godfrey S. B. Ajoudani A. Bianchi M. Catalano M. G. Grioli G. Bicchi A. (2014). “Translating soft robotics technologies to prosthetics: early results with a softhand prosthesis,” in MEC 14: Myoelectric Controls Symposium (Fredericton, NB).
Cited by
Sorted by citations
Losey, D. P., McDonald, C. G., Battaglia, E., & O’Malley, M. K. (2018) · Applied Mechanics Reviews · doi:10.1115/1.4039145 · Cited by 400 · see citing works
Shared human–robot proportional control of a dexterous myoelectric prosthesis
Zhuang, K., Sommer, N., Mendez, V., Aryan, S., Formento, E., D’Anna, E., Artoni, F., Petrini, F. M., Granata, G., Cannaviello, G., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0093-5 · Cited by 140 · see citing works
Current Solutions and Future Trends for Robotic Prosthetic Hands
Mendez, V., Iberite, F., Shokur, S., & Micera, S. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-071020-104336 · Cited by 112 · see citing works
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.
Ghafoor, U., Kim, S., & Hong, K.-S. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00059 · Cited by 88 · see citing works
“…due to its better classification of EMG signals (user intentions) using various methods, i.e., linear discriminant analysis classifier, tacit learning system, deep learning with convolutional neural network (Adewuyi et al., 2016; Atzori et al., 2016; Fani et al., 2016; Oyama et al., 2016), etc.”
The Rice Haptic Rocker: Skin stretch haptic feedback with the Pisa/IIT SoftHand
Battaglia, E., Clark, J. P., Bianchi, M., Catalano, M. G., Bicchi, A., & O’Malley, M. K. (2017) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2017.7989848 · Cited by 76 · see citing works
Fani, S., Ciotti, S., Catalano, M. G., Grioli, G., Tognetti, A., Valenza, G., Ajoudani, A., & Bianchi, M. (2018) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2017.2741579 · Cited by 55 · see citing works
Fu, Q., & Santello, M. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00071 · Cited by 37 · see citing works
A hybrid Body-Machine Interface integrating signals from muscles and motions
Rizzoglio, F., Pierella, C., Santis, D. D., Mussa-Ivaldi, F. A., & Casadio, M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab9b6c · Cited by 32 · see citing works
Prediction of Reach Goals in Depth and Direction from the Parietal Cortex
Filippini, M., Breveglieri, R., Hadjidimitrakis, K., Bosco, A., & Fattori, P. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.090 · Cited by 27 · see citing works
Shenoy, P., Sompur, V., & Varadhan, S. K. M. (2022) · IEEE Access · doi:10.1109/access.2022.3168674 · Cited by 16 · see citing works
EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses
Khadivar, F., Mendez, V., Correia, C., Batzianoulis, I., Billard, A., & Micera, S. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca35f · Cited by 15 · see citing works
Paek, A. Y., Gailey, A. S., Parikh, P. J., Santello, M., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab4063 · Cited by 15 · see citing works
Sharma, N., Prakash, A., Sahi, A. K., Sharma, N., & Sharma, S. (2020) · Instrumentation Science & Technology · doi:10.1080/10739149.2020.1804932 · Cited by 13 · see citing works
Pagnanelli, G., Zinelli, L., Lepora, N. F., Catalano, M. G., Bicchi, A., & Bianchi, M. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3522149 · Cited by 11 · see citing works
Shah, G., Sharma, A., Joshi, D., & Rathor, A. S. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110094 · Cited by 11 · see citing works
Grasp Performance of a Soft Synergy-Based Prosthetic Hand: A Pilot Study
Gailey, A. S., Godfrey, S. B., Breighner, R. E., Andrews, K. L., Zhao, K. D., Bicchi, A., & Santello, M. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2737539 · Cited by 11 · see citing works
EMG-Controlled Non-Anthropomorphic Hand Teleoperation Using a Continuous Teleoperation Subspace
Meeker, C., & Ciocarlie, M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794108 · Cited by 11 · see citing works
Evoked Tactile Feedback and Control Scheme on Functional Utility of Prosthetic Hand
Vargas, L., Huang, H., Zhu, Y., & Hu, X. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3139147 · Cited by 9 · see citing works
Posture similarity index: a method to compare hand postures in synergy space
Bhatt, N., & Varadhan, S. K. M. (2018) · PeerJ · doi:10.7717/peerj.6078 · Cited by 7 · see citing works
Vishwakarma, S. K., Lakkireddy, C., Bardia, A., Paspala, S. A. B., & Khan, A. A. (2019) · Materials Science and Engineering C · doi:10.1016/j.msec.2019.04.028 · Cited by 7 · see citing works
A Robust Control Strategy for Sensorless Under-Tendon-Driven Prosthetic Hands
Fajardo, J. V., Cardona, D., Maldonado, G., Ferman, V., & Rohmer, E. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659369 · Cited by 3 · see citing works
Kinematic hand synergies differ between reach-and-grasp and functional object manipulation
West, A. M., & Hogan, N. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00101.2024 · Cited by 5 · see citing works
Fu, Q., & Santello, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-38740-2_4 · Cited by 2 · see citing works
EMG-Controlled Hand Teleoperation Using a Continuous Teleoperation Subspace.Preprint
Meeker, C., & Ciocarlie, M. (2018) · arXiv (Cornell University) · Cited by 2 · see citing works
Human-inspired compliance discrimination with a multi-degrees of freedom robotic manipulator
Zinelli, L., Pagnanelli, G., & Bianchi, M. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217847 · Cited by 1 · see citing works
Exploring A Stylus-Based Haptic Interface to Reflect on Human-Robot Interaction
Domingos, D. E., Jünger, K., Sörries, P., & Zwick, C. (2025) · International Conference on Culture and Computer Science (KUI) · doi:10.1145/3769526.3769631
Zhu, S., Huang, X., Sun, S., Han, Y., Yao, T., & Cao, L. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00721-6
- Authors
- Fani, S., Bianchi, M., Jain, S., Pimenta Neto, J. S., Boege, S., Grioli, G., Bicchi, A., & Santello, M.
- Affiliations
- University of Pisa (IT)
- PES University (IN)
- Arizona State University (US)
- Italian Institute of Technology (IT)
- Corresponding author
- Fani, Simone
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2016-10-17
- Volume
- 10
- Pages
- 11-11
- ISSN
- 1662-5218 (ISSN-L)
- PMC
- PMC5066092
- OpenAlex
- W2529411793
- Cited by
- 31
- References
- 35
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 10 · 2017: 3
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neurorobotics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Arizona State University Library Digital Repository (Arizona State University) (submittedVersion, cc-by)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Grainger Foundation
- European Research Council (ERC-291166)
- Horizon 2020 (688857)
- Seventh Framework Programme (601165)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD081938)
- 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
- myoelectric prosthetic hand, surface electromyography, underactuated robotic hand, reach-to-grasp, finger kinematics, flexor digitorum superficialis, extensor digitorum communis, prosthesis abandonment, human-robot interaction
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
