Development of an Implantable Myoelectric Sensor for Advanced Prosthesis Control
Open accessAbstract
Modern hand and wrist prostheses afford a high level of mechanical sophistication, but the ability to control them in an intuitive and repeatable manner lags. Commercially available systems using surface electromyographic (EMG) or myoelectric control can supply at best two degrees of freedom (DOF), most often sequentially controlled. This limitation is partially due to the nature of surface‐recorded EMG, for which the signal contains components from multiple muscle sources. We report here on the development of an implantable myoelectric sensor using EMG sensors that can be chronically implanted into an amputee's residual muscles. Because sensing occurs at the source of muscle contraction, a single principal component of EMG is detected by each sensor, corresponding to intent to move a particular effector. This system can potentially provide independent signal sources for control of individual effectors within a limb prosthesis. The use of implanted devices supports inter‐day signal repeatability. We report on efforts in preparation for human clinical trials, including animal testing, and a first‐in‐human proof of principle demonstration where the subject was able to intuitively and simultaneously control two DOF in a hand and wrist prosthesis.
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?
Penetrating electrodes are mostly rigid enough to maintain structural integrity in muscle.
“The penetrating electrodes are mostly rigid enough to maintain their structural integrity in the muscle.[367,368]”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [367]
The Materials and Methods section (IMES technical description; page unavailable) describes electrodes on a ceramic-packaged implant, and the Results (Animal experiments; page unavailable) report that implanted devices operated without problems and did not migrate in chronic animal studies. Those observations support stability of this particular implant in muscle, but the paper does not establish that penetrating electrodes are mostly rigid or that rigidity maintains their structural integrity. (LLM, conf=0.78)
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
Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050
Ziegler‐Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2007.11.005 · Cited by 2,823 · see citing works
Muscles Alive—their functions revealed by electromyography
(1963) · Postgraduate Medical Journal · doi:10.1136/pgmj.39.449.162-b · Cited by 1,780 · 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
A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control
Ajiboye, A. B., & Weir, R. F. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.847357 · Cited by 415 · see citing works
Self-citationImplantable Myoelectric Sensors (IMESs) for Intramuscular Electromyogram Recording
Weir, R. F. F., Troyk, P. R., DeMichele, G. A., Kerns, D., Schorsch, J. F., & Maas, H. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2005942 · Cited by 264 · see citing works
Self-citationMUSCLES ALIVE-THEIR FUNCTIONS REVEALED BY ELECTROMYOGRAPHY
deVries, H. A. (1976) · Medicine & Science in Sports & Exercise · doi:10.1249/00005768-197621000-00002 · Cited by 82 · see citing works
Continuous Detection and Decoding of Dexterous Finger Flexions With Implantable MyoElectric Sensors
Baker, J. J., Scheme, E., Englehart, K. B., Hutchinson, D. T., & Greger, B. E. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2047590 · Cited by 78 · see citing works
A Statistical Study of 12,000 New Amputees
Glattly, H. W. (1964) · Southern Medical Journal · doi:10.1097/00007611-196411000-00024 · Cited by 49 · see citing works
Weir, R. F. F., Troyk, P. R., DeMichele, G. A., & Kerns, D. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1616206 · Cited by 41 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics
Heng, W., Solomon, S., & Gao, W. (2022) · Advanced Materials · doi:10.1002/adma.202107902 · Cited by 609 · see citing works
Control of upper limb prostheses: terminology and proportional myoelectric control-a review
Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., & Parker, P. A. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2196711 · Cited by 495 · see citing works
Toward higher-performance bionic limbs for wider clinical use
Farina, D., Vujaklija, I., Brånemark, R., Bull, A. M. J., Dietl, H., Graimann, B., Hargrove, L. J., Hoffmann, K.-P., Huang, H., Ingvarsson, T., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00732-x · Cited by 263 · see citing works
Self-citationFleming, A., Stafford, N. E., Huang, S., Hu, X., Ferris, D. P., & Huang, H. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac1176 · Cited by 221 · 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
New developments in prosthetic arm systems
Vujaklija, I., Farina, D., & Aszmann, O. C. (2016) · Orthopedic Research and Reviews · doi:10.2147/orr.s71468 · Cited by 181 · see citing works
Surveying the interest of individuals with upper limb loss in novel prosthetic control techniques
Engdahl, S. M., Christie, B. P., Kelly, B. M., Davis, A. J., Chestek, C. A., & Gates, D. H. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-015-0044-2 · Cited by 177 · see citing works
Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition
Yang, X., Sun, X., Zhou, D., Li, Y., & Liu, H. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2829913 · Cited by 156 · 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
Real-time simultaneous and proportional myoelectric control using intramuscular EMG
Smith, L. H., Kuiken, T., & Hargrove, L. J. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/6/066013 · Cited by 128 · see citing works
Visconti, P., Gaetani, F., Zappatore, G. A., & Primiceri, P. (2018) · International Journal on Smart Sensing and Intelligent Systems · doi:10.21307/ijssis-2018-005 · Cited by 122 · see citing works
Nizamis, K., Athanasiou, A., Almpani, S., Dimitrousis, C., & Astaras, A. (2021) · Sensors · doi:10.3390/s21062084 · Cited by 118 · see citing works
Matrone, G. C., Cipriani, C., Carrozza, M. C., & Magenes, G. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-40 · Cited by 115 · see citing works
Huang, S., & Ferris, D. P. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-55 · Cited by 101 · see citing works
Cipriani, C., Segil, J. L., Birdwell, J. A., & Weir, R. F. F. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2301234 · Cited by 83 · see citing works
Self-citationKamavuako, E. N., Farina, D., Yoshida, K., & Jensen, W. (2011) · Annals of Biomedical Engineering · doi:10.1007/s10439-011-0438-7 · Cited by 73 · see citing works
Developments in the human machine interface technologies and their applications: a review
Singh, H. P., & Kumar, P. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.1936237 · Cited by 65 · see citing works
Continuous Heart Volume Monitoring by Fully Implantable Soft Strain Sensor
Dual, S. A., Zambrano, B. L., Sündermann, S., Cesarovic, N., Kron, M., Magkoutas, K., Hengsteler, J., Falk, V., Starck, C. T., Meboldt, M., et al. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.202000855 · Cited by 62 · see citing works
Recent Trends in EMG-Based Control Methods for Assistive Robots
Gopura, R. A. R. C., Bandara, D. S. V., Gunasekara, J. M. P., & S., T. (2013) · InTech eBooks · doi:10.5772/56174 · Cited by 59 · see citing works
Calado, A., Soares, F., & Matos, D. (2019) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc.2019.8733629 · Cited by 59 · see citing works
The effect of time on EMG classification of hand motions in able-bodied and transradial amputees
Waris, A., Niazi, I. K., Jamil, M., Gilani, O., Englehart, K. B., Jensen, W., Shafique, M., & Kamavuako, E. N. (2018) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2018.04.004 · Cited by 53 · see citing works
Bergmeister, K. D., Vujaklija, I., Muceli, S., Sturma, A., Hruby, L. A., Prahm, C., Riedl, O., Salminger, S., Manzano-Szalai, K., Aman, M., et al. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00421 · Cited by 52 · see citing works
Biosensing Technologies for Medical Applications, Manufacturing, and Regenerative Medicine
Shafiee, A., Ghadiri, E., Kassis, J. N., Zarandi, N. P., & Atala, A. (2018) · Current Stem Cell Reports · doi:10.1007/s40778-018-0123-y · Cited by 51 · see citing works
Explainable Deep Learning Model for EMG-Based Finger Angle Estimation Using Attention
Lee, H., Kim, D., & Park, Y. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3188275 · Cited by 49 · see citing works
Problems of a trajectory planning in autonomous navigation systems based on technical vision and AI
Zhilenkov, A., & Epifantsev, I. R. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317265 · Cited by 47 · see citing works
Motor control and learning with lower-limb myoelectric control in amputees
Alcaide-Aguirre, R. E., Morgenroth, D. C., & Ferris, D. P. (2013) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2012.06.0115 · Cited by 45 · see citing works
High productivity numerical computations for gas dynamics modelling based on DFT and approximation
Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317117 · Cited by 42 · see citing works
System of autonomous navigation of the drone in difficult conditions of the forest trails
Zhilenkov, A., & Epifantsev, I. R. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317266 · Cited by 42 · see citing works
Jowett, N., Kearney, R. E., Knox, C., & Hadlock, T. A. (2018) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000005164 · Cited by 42 · see citing works
sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand
Jiang, Y., Togane, M., Lu, B., & Yokoi, H. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00033 · Cited by 39 · see citing works
The use of IMU MEMS-sensors for designing of motion capture system for control of robotic objects
Ivanov, A. V., & Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317231 · Cited by 39 · see citing works
Sun, X., Yang, X., Zhu, X., & Liu, H. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2778243 · Cited by 36 · see citing works
Acquisition and processing data system based on technologies of inertial capture of the movement
Ivanov, A. V., & Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317229 · Cited by 31 · see citing works
Birdwell, J. A., Hargrove, L. J., Weir, R. F. F., & Kuiken, T. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2344854 · Cited by 30 · see citing works
Self-citationKent, B. A., Lavery, J., & Engeberg, E. D. (2014) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(14)60044-5 · Cited by 28 · see citing works
Ku, I., Lee, G., Park, C. Y., Lee, J. H., & Jeong, E. C. (2019) · Archives of Plastic Surgery · doi:10.5999/aps.2018.01375 · Cited by 28 · see citing works
Surgical and technological advances in the management of upper limb amputees
Geary, M. B., Gaston, R. G., & Loeffler, B. J. (2021) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b3.bjj-2020-1184.r1 · Cited by 27 · see citing works
Song, H., Israel, E. A., Gutierrez-Arango, S., Teng, A. C., Srinivasan, S. S., Freed, L. E., & Herr, H. (2022) · Communications Medicine · doi:10.1038/s43856-022-00162-z · Cited by 23 · see citing works
Smith, L. H., Kuiken, T., & Hargrove, L. J. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2410755 · Cited by 23 · see citing works
Synthesis of model of hardware realization of LIF-model of biological neuron on the basis of FPGA
Slepova, L. O., & Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317256 · Cited by 23 · see citing works
Intelligent Knee Prostheses: A Systematic Review of Control Strategies
Li, L., Wang, X., Meng, Q., Chen, C., Sun, J., & Yu, H. (2022) · Journal of Bionic Engineering · doi:10.1007/s42235-022-00169-1 · Cited by 22 · see citing works
Motorized Biomechatronic Upper and Lower Limb Prostheses—Clinically Relevant Outcomes
Lechler, K., Frossard, B., Whelan, L. R., Langlois, D., Müller, R., & Kristjánsson, K. (2018) · PM&R · doi:10.1016/j.pmrj.2018.06.015 · Cited by 22 · see citing works
Becerra-Fajardo, L., Schmidbauer, M., & Ivorra, A. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2623483 · Cited by 21 · see citing works
Artificial Muscle for Reanimation of the Paralyzed Face
Ledgerwood, L. G., Tinling, S. P., Senders, C. W., Wong-Foy, A., Prahlad, H., & Tollefson, T. T. (2012) · Archives of Facial Plastic Surgery · doi:10.1001/archfacial.2012.696 · Cited by 21 · see citing works
Potluri, C., Anugolu, M., Naidu, D. S., Schoen, M. P., & Chiu, S. C. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.08.007 · Cited by 21 · see citing works
The use of convolution artificial neural networks for drones autonomous trajectory planning
Zhilenkov, A., & Epifantsev, I. R. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317268 · Cited by 18 · see citing works
Becerra-Fajardo, L., Krob, M. O., Minguillón, J., Rodrigues, C., Welsch, C., Tudela-Pi, M., Comerma-Montells, A., Barroso, F. O., Schneider, A., & Ivorra, A. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01033-3 · Cited by 16 · see citing works
A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN
Waris, A., Rehman, M. Z. U., Niazi, I. K., Jochumsen, M., Englehart, K. B., Jensen, W., Haavik, H., & Kamavuako, E. N. (2020) · Sensors · doi:10.3390/s20123385 · Cited by 15 · see citing works
Karczewski, A. M., Dingle, A. M., & Poore, S. O. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.711028 · Cited by 13 · see citing works
Lessons from Gracilis Free Tissue Transfer for Facial Paralysis
Miller, M. Q., Jowett, N., & Hadlock, T. A. (2021) · Facial Plastic Surgery Clinics of North America · doi:10.1016/j.fsc.2021.03.001 · Cited by 12 · see citing works
Gstoettner, C., Festin, C., Prahm, C., Bergmeister, K. D., Salminger, S., Sturma, A., Höfer, C., Russold, M., Howard, C. L., McDonnall, D., et al. (2022) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000002135 · Cited by 12 · see citing works
Zhilenkov, A., & Kotlyarevskaya, M. V. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317267 · Cited by 12 · see citing works
Tereshenko, V., Dotzauer, D. C., Schmoll, M., Harnoncourt, L., Rojas, G. C., Gfrerer, L., Eberlin, K. R., Austen, W. G., Blumer, R., Farina, D., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj3872 · Cited by 10 · see citing works
Control of Bionic Robot Leg Performance with Proportional Integral and Derivative Controller
Widhiada, I. W., Nindhia, T. G. T., Widiyarta, I. M., & Budiarsa, I. N. (2020) · International Journal of Mechanical Engineering and Robotics Research · doi:10.18178/ijmerr.9.10.1447-1450 · Cited by 10 · see citing works
Recent Advances in Biosensing in Tissue Engineering and Regenerative Medicine
Banigo, A. T., Nnadiekwe, C. A., & Beasi, E. M. (2022) · Biomedical engineering · doi:10.5772/intechopen.104922 · Cited by 10 · see citing works
Kelley, M. A., Benz, H. L., Engdahl, S. M., & Bridges, J. F. P. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1626231 · Cited by 10 · see citing works
Design of Adaptive LQR Control Based on Improved Grey Wolf Optimization for Prosthetic Hand
Ahmed, K., Aly, A. A., & Elhabib, M. O. (2025) · Biomimetics · doi:10.3390/biomimetics10070423 · Cited by 9 · see citing works
The prospects of use of deep learning neural networks in problems of dynamic images recognition
Ivanov, A., & Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317230 · Cited by 9 · see citing works
Becerra-Fajardo, L., Minguillón, J., Krob, M. O., Rodrigues, C., González-Sánchez, M., Megía‐García, Á., Redondo-Galán, C., Henares, F. G., Comerma-Montells, A., del‐Ama, A. J., et al. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01295-5 · Cited by 9 · see citing works
Low-power polling mode of the next-generation IMES2 Implantable wireless EMG sensor
DeMichele, G. A., Hu, Z., Troyk, P. R., Chen, H., & Weir, R. F. F. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944274 · Cited by 9 · see citing works
Self-citationMuscle Activation Source Model-based sEMG Signal Decomposition and Recognition of Interface Rotation
Kim, M., & Chung, W. K. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593448 · Cited by 9 · see citing works
Robotic Prostheses and Neuromuscular Interfaces: A Review of Design and Technological Trends
Batista, P. G., Vieira, A. C., & Gaspar, P. D. (2025) · Machines · doi:10.3390/machines13090804 · Cited by 8 · see citing works
Enhancing the versatility of wireless biopotential acquisition for myoelectric prosthetic control
Bercich, R. A., Wang, Z., Mei, H., Hammer, L. H., Seburn, K. L., Hargrove, L. J., & Irazoqui, P. P. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046012 · Cited by 8 · see citing works
Vujaklija, I., & Farina, D. (2022) · Springer eBooks · doi:10.1007/978-3-031-10355-1_41 · Cited by 8 · see citing works
Design trends in actuated lower-limb prosthetic systems: a narrative review
Sun, H., He, C., & Vujaklija, I. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2279999 · Cited by 7 · see citing works
Alazrai, R., Khalifeh, A. F., Alnuman, N., Alabed, D., & Mowafi, Y. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590704 · Cited by 7 · see citing works
Somatosensory restoration and neural control strategies in lower-limb prostheses
Mariscal, D. M., Driscoll, B., Huang, H., & Fisher, L. E. (2025) · npj Biomedical Innovations. · doi:10.1038/s44385-025-00050-w · Cited by 6 · see citing works
Tokunaga, D., Nishikawa, S., & Kiguchi, K. (2023) · IEEE Access · doi:10.1109/access.2023.3312575 · Cited by 6 · see citing works
Kneisz, L., Unger, E., Lanmüller, H., & Mayr, W. (2015) · Artificial Organs · doi:10.1111/aor.12626 · Cited by 6 · see citing works
Manipulation and grasping control for a hand-eye robot system using sensory-motor fusion
Hu, Y., Lin, G., Yang, C., Li, Z., & Su, C. (2015) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2015.7418792 · Cited by 6 · see citing works
sEMG-based approach for estimating wrist and fingers joint angles using discrete wavelet transform
Alazrai, R., Alabed, D., Alnuman, N., Khalifeh, A. F., & Mowafi, Y. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843519 · Cited by 5 · see citing works
Continuous estimation of hand's joint angles from sEMG using wavelet-based features and SVR
Alazrai, R., Alabed, D., Alnuman, N., Khalifeh, A. F., & Mowafi, Y. (2016) · Workshop on ICTs for improving Patients Rehabilitation Research Techniques (REHAB) · doi:10.1145/3051488.3051498 · Cited by 5 · see citing works
Stavdahl, Ø., Mugaas, T., Ottermo, M. V., Magne, T., & Kyberd, P. (2019) · JPO Journal of Prosthetics and Orthotics · doi:10.1097/jpo.0000000000000296 · Cited by 5 · see citing works
Huang, S. (2014) · Deep Blue (University of Michigan) · Cited by 5 · see citing works
Sensing and Stimulating Electrodes for Electroceuticals
Liu, X., Wang, K., González‐González, M. A., Romero‐Ortega, M. I., & Wallace, G. G. (2022) · Frontiers in Sensors · doi:10.3389/fsens.2022.873862 · Cited by 4 · see citing works
Intramuscular microelectrode arrays enable highly accurate neural decoding of finger tasks
Grison, A., Ibáñez, J., Muceli, S., Kundu, A., Baracat, F., Indiveri, G., Donati, E., & Farina, D. (2026) · Interface Focus · doi:10.1098/rsfs.2025.0063 · Cited by 4 · see citing works
Ghaderi, P., Karimimehr, S., Andani, M. E., & Marateb, H. R. (2015) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2015.7404148 · Cited by 4 · see citing works
Tyler, D. J., Polasek, K. H., & Schiefer, M. A. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802653-3.00112-3 · Cited by 4 · see citing works
Modeling and simulating of unclad fiber-optic biosensor based on localized surface plasmon resonance
Azzahrani, A. S., Fakhri, M. A., Salim, E. T., Ali, A. B. M., Tariq, S. M., Majeed, S. K., Salim, Z. T., & Gopinath, S. C. B. (2024) · Journal of Optics · doi:10.1007/s12596-024-02130-3 · Cited by 3 · see citing works
Chi, A., Smith, S. H., Womack, I., & Armiger, R. S. (2018) · Current Trauma Reports · doi:10.1007/s40719-018-0142-2 · Cited by 3 · see citing works
Alazrai, R., Daoud, M. I., Khalifeh, A. F., Alnuman, N., Mowafi, Y., & Alabed, D. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-16785-1_3 · Cited by 3 · see citing works
4. Upper and Lower Limb Robotic Prostheses
Pilarski, P. M., & Hebert, J. S. (2017) · Rehabilitation science in practice series · doi:10.1201/9781315368788-5 · Cited by 3 · see citing works
Development of electric hand prothesis controlled by voice commands and muscle sensors
Guevara-Roselló, J., & Murillo-Aldecoba, J. E. (2024) · Revista Tecnología en Marcha · doi:10.18845/tm.v37i5.7223 · Cited by 3 · see citing works
Artificial Organs 2011: A Year in Review
Malchesky, P. S. (2012) · Artificial Organs · doi:10.1111/j.1525-1594.2012.01438.x · Cited by 3 · see citing works
Evaluation of a Novel Myoelectric Training Device
Arenas, J. A. (2015) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/2v2k-7s78 · Cited by 3 · see citing works
Real time Electronic Control of Multi Fingered Hand Based on Sensors
Shyamala, B. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.353 · Cited by 3 · see citing works
Problem of automatic recognition of physical activity from surface electromyography signals
Zhilenkov, A., Kuznetsova, N. I., & Zernov, O. I. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317264 · Cited by 3 · see citing works
Electrode Design for Electrotactile Feedback With Reduced Interference to Myoelectric Signal
Yang, D., Huang, Q., Jiang, Z., & Jiang, L. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076092 · Cited by 3 · see citing works
Trezise, T. (2011) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 3 · see citing works
Lee, H., Lee, Y., Jiang, L., Chen, J., Lam, W. L., & Lo, Y. T. (2025) · Advanced technology in neuroscience . · doi:10.4103/atn.atn-d-25-00007 · Cited by 2 · see citing works
Implanted myoneural interface for artificial hand control
Prabhav, N. R., Devasahayam, S. R., & Babu, K. S. (2014) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2014.059057 · Cited by 2 · see citing works
Classification of hand movements related to grasp by using EMG signals
Akben, S. B. (2015) · National Biomedical Engineering Meeting · doi:10.1109/biyomut.2015.7369445 · Cited by 2 · see citing works
Kuznetsova, N. I., Zhilenkov, A., & Zernov, O. I. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317237 · Cited by 2 · see citing works
Segil, J. L., Lukyanenko, P., Lambrecht, J., Weir, R. F. F., & Tyler, D. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630845 · Cited by 2 · see citing works
Self-citationTraining Strategy and sEMG Sensor Positioning for Finger Force Estimation at Various Elbow Angles
Cho, M., Cho, Y., & Kim, K.-S. (2022) · International Journal of Control Automation and Systems · doi:10.1007/s12555-021-0277-8 · Cited by 2 · see citing works
A ΔC Detection Circuit for Capacitive Sensing Based Prosthetic Control Applications
Nakum, B., Wani, M. I., Ehshan, K. M., & Malik, S. (2023) · IEEE Applied Sensing Conference (APSCON) · doi:10.1109/apscon56343.2023.10101055 · Cited by 1 · see citing works
Coppey, G., Andreu, D., & Guiraud, D. (2012) · HAL · Cited by 1 · see citing works
The History and Future of LEGS
Tepe, V., Salas-Snyder, S., & Peterson, C. M. (2017) · Springer eBooks · doi:10.1007/978-1-4939-7247-0_1 · Cited by 1 · see citing works
Implementing a Bluetooth EMG System Comprising a Subcutaneous Sensor and the Associated Transceiver
Calado, A., Errico, V., Boccardo, N., Canepa, M., Gruppioni, E., & Saggio, G. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-08136-1_35 · Cited by 1 · see citing works
Çelik, M. S., Tepe, C., & Eminoğlu, İ. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104732 · Cited by 1 · see citing works
- Authors
- Merrill, D. R., Lockhart, J., Troyk, P. R., Weir, R. F., & Hankin, D. L.
- Affiliations
- Alfred Mann Foundation (US)
- Shirley Ryan AbilityLab (US)
- Corresponding author
- Merrill, Daniel R.
- ORCID
- Weir, Richard F.
- Venue
- Artificial Organs
- Publisher
- Wiley
- Published
- 2011-03
- Volume
- 35
- Issue
- 3
- Pages
- 249-252
- ISSN
- 0160-564X (ISSN-L)
- 1525-1594
- PMC
- PMC3768255
- OpenAlex
- W2097755153
- Cited by
- 117
- References
- 9
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 11 · 2021: 10 · 2020: 5 · 2019: 8 · 2018: 20 · 2017: 7 · 2016: 8 · 2015: 10 · 2014: 8 · 2013: 3 · 2012: 6
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Artificial Organs (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB001672)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- prosthetic hand control, wrist prosthesis, chronic implantation, surface electromyography, multi-degree-of-freedom control, amputees, signal repeatability
- MeSH terms
- Animals, Haplorhini, Artificial Limbs, Cats, Electromyography, Humans, Prosthesis Design, Prosthesis Implantation
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
