Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series
Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuit...
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| Published in | Journal of neuroengineering and rehabilitation Vol. 18; no. 1; pp. 50 - 15 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
18.03.2021
BioMed Central BMC |
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| Online Access | Get full text |
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| Abstract | Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control.
Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability.
In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. |
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| AbstractList | Background Current commercial prosthetic hand controllers limit patients’ ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Methods Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. Results and conclusions In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7–9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. Abstract Background Current commercial prosthetic hand controllers limit patients’ ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Methods Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. Results and conclusions In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7–9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. Background Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Methods Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. Results and conclusions In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. Keywords: Electromyography, Prosthetic control, Virtual reality, Interpolation Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control.BACKGROUNDCurrent commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control.Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability.METHODSTwo trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability.In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times.RESULTS AND CONCLUSIONSIn 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times. |
| ArticleNumber | 50 |
| Audience | Academic |
| Author | Tyler, Dustin J. Dewald, Hendrik Adriaan Kirsch, Robert F. Lambrecht, Joris Lukyanenko, Platon Williams, Matthew R. |
| Author_xml | – sequence: 1 givenname: Platon orcidid: 0000-0002-1333-3790 surname: Lukyanenko fullname: Lukyanenko, Platon – sequence: 2 givenname: Hendrik Adriaan orcidid: 0000-0002-0478-7985 surname: Dewald fullname: Dewald, Hendrik Adriaan – sequence: 3 givenname: Joris orcidid: 0000-0002-9370-0000 surname: Lambrecht fullname: Lambrecht, Joris – sequence: 4 givenname: Robert F. orcidid: 0000-0003-2564-1800 surname: Kirsch fullname: Kirsch, Robert F. – sequence: 5 givenname: Dustin J. orcidid: 0000-0002-2298-8510 surname: Tyler fullname: Tyler, Dustin J. – sequence: 6 givenname: Matthew R. orcidid: 0000-0002-3402-249X surname: Williams fullname: Williams, Matthew R. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33736656$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1088_1741_2552_ad56c9 crossref_primary_10_1186_s12984_025_01561_8 crossref_primary_10_1371_journal_pone_0291279 crossref_primary_10_1126_scirobotics_adf7360 crossref_primary_10_1088_1741_2552_accb0c crossref_primary_10_1109_TNSRE_2022_3157710 crossref_primary_10_1038_s41598_025_87048_x crossref_primary_10_1002_mus_28029 crossref_primary_10_1002_adma_202107902 crossref_primary_10_3390_electronics11142271 crossref_primary_10_3390_biomimetics8030328 crossref_primary_10_3389_fnbot_2022_902162 crossref_primary_10_1002_aisy_202400569 |
| Cites_doi | 10.1080/00222895.1986.10735388 10.1109/TNSRE.2011.2178039 10.1109/TBME.2012.2232293 10.1109/10.204774 10.1109/TRO.2009.2039378 10.1016/j.jelekin.2008.11.013 10.1126/scitranslmed.aaf5187 10.1109/IEMBS.2009.5332745 10.1145/355780.355786 10.3389/fncom.2014.00046 10.1109/TNSRE.2013.2287383 10.1186/s12984-015-0016-6 10.1109/TBME.2011.2155063 10.1126/scitranslmed.aay2857 10.1109/TNSRE.2011.2108667 10.1109/TNSRE.2014.2306000 10.1126/scirobotics.aat3630 10.1523/JNEUROSCI.0830-06.2006 10.1126/scitranslmed.3008933 10.1682/JRRD.2014.05.0134 10.1109/MLSP.2012.6349712 10.1109/TNSRE.2013.2278411 10.1109/TNSRE.2014.2305520 10.1109/NER.2015.7146707 10.1109/TNSRE.2007.891391 10.1016/j.jneumeth.2014.07.016 10.1109/EMBC.2012.6346734 10.1126/scitranslmed.aao6990 10.1371/journal.pone.0186318 10.1109/TBME.2006.889192 10.1007/s40137-013-0044-8 10.1186/1743-0003-9-42 10.1109/TSMCA.2009.2028239 10.1682/JRRD.2013.02.0056 10.1126/scitranslmed.3008669 10.1016/S1050-6411(02)00026-3 10.1152/jn.00559.2009 10.1186/s12984-019-0607-8 10.1109/86.481972 10.1109/ICMA.2011.5985757 10.1109/TBME.2008.2007967 10.1109/TBME.2015.2469741 10.1097/00008526-199601000-00003 10.1371/journal.pone.0161678 10.1016/j.apmr.2014.01.028 10.3389/fnhum.2018.00352 10.1186/1743-0003-9-40 |
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| References | JM Hahne (833_CR12) 2014; 22 GC Matrone (833_CR27) 2012; 9 S Muceli (833_CR37) 2010; 103 H Dewald (833_CR10) 2019; 16 AM Simon (833_CR19) 2011; 58 AD Roche (833_CR1) 2014; 2 LJ Hargrove (833_CR43) 2018; 15 A Hargrove (833_CR46) 2007; 54 EL Graczyk (833_CR54) 2016; 8 TR Farrell (833_CR11) 2007; 15 M Zardoshti-Kermani (833_CR40) 1995; 3 833_CR18 A D’Avella (833_CR26) 2006; 26 S Muceli (833_CR48) 2012; 20 DA Gabriel (833_CR42) 2002; 12 DJ Atkins (833_CR3) 1996 DA Gabriel (833_CR49) 2002; 12 JM Hahne (833_CR22) 2018 N Jiang (833_CR17) 2014; 22 WD Memberg (833_CR34) 2014; 95 D Farina (833_CR51) 2014; 22 JMM Hahne (833_CR15) 2012 PK Artemiadis (833_CR28) 2010; 26 833_CR29 C Cipriani (833_CR13) 2011; 19 JL Segil (833_CR53) 2015; 52 M Ortiz-Catalan (833_CR9) 2014; 6 T Kapelner (833_CR25) 2015 PD Marasco (833_CR7) 2018 H Akima (833_CR33) 1978; 4 N Jiang (833_CR21) 2012; 9 TM Lam (833_CR52) 2009; 39 M Nowak (833_CR31) 2016; 11 DJ Berger (833_CR45) 2014; 8 B Hudgins (833_CR20) 1993; 40 AR Chapman (833_CR36) 2010; 20 MR Williams (833_CR38) 2015; 12 PF Pasquina (833_CR2) 2015 N Jiang (833_CR24) 2014; 22 L Mitas (833_CR32) 1999 833_CR35 L Resnik (833_CR4) 2014; 51 833_CR39 833_CR6 MS Johannes (833_CR5) 2011; 30 LH Smith (833_CR30) 2016; 63 SP Moore (833_CR50) 1986; 18 SP Moore (833_CR41) 2013; 18 N Jiang (833_CR23) 2009; 56 J Young (833_CR14) 2013; 60 C Ishii (833_CR16) 2011; 2011 833_CR44 833_CR47 DM Page (833_CR55) 2018; 12 DW Tan (833_CR8) 2014 |
| References_xml | – volume: 18 start-page: 397 issue: 4 year: 1986 ident: 833_CR50 publication-title: J Mot Behav doi: 10.1080/00222895.1986.10735388 – volume: 20 start-page: 371 issue: 3 year: 2012 ident: 833_CR48 publication-title: IEEE Trans Neural Syst Rehab Eng. doi: 10.1109/TNSRE.2011.2178039 – volume: 60 start-page: 1250 issue: 5 year: 2013 ident: 833_CR14 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2012.2232293 – volume: 40 start-page: 82 issue: 1 year: 1993 ident: 833_CR20 publication-title: IEEE Trans Biomed Eng doi: 10.1109/10.204774 – volume: 26 start-page: 393 issue: 2 year: 2010 ident: 833_CR28 publication-title: IEEE Trans Rob doi: 10.1109/TRO.2009.2039378 – volume: 20 start-page: 108 issue: 1 year: 2010 ident: 833_CR36 publication-title: J Electromyogr Kinesiol doi: 10.1016/j.jelekin.2008.11.013 – volume: 8 start-page: 1 issue: 362 year: 2016 ident: 833_CR54 publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aaf5187 – ident: 833_CR18 doi: 10.1109/IEMBS.2009.5332745 – volume: 4 start-page: 148 year: 1978 ident: 833_CR33 publication-title: ACM Trans Math Softw doi: 10.1145/355780.355786 – volume: 8 start-page: 1 issue: April year: 2014 ident: 833_CR45 publication-title: Front. Comput. Neurosci. doi: 10.3389/fncom.2014.00046 – ident: 833_CR6 – volume: 22 start-page: 549 issue: 3 year: 2014 ident: 833_CR24 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2013.2287383 – volume: 12 start-page: 485 issue: 1 year: 2015 ident: 833_CR38 publication-title: J NeuroEng Rehabil doi: 10.1186/s12984-015-0016-6 – volume: 30 start-page: 207 issue: 3 year: 2011 ident: 833_CR5 publication-title: Johns Hopkins APL Technical Digest – volume: 58 start-page: 2360 issue: 8 year: 2011 ident: 833_CR19 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2011.2155063 – ident: 833_CR47 doi: 10.1126/scitranslmed.aay2857 – volume: 15 start-page: 21 issue: Suppl 1 60 year: 2018 ident: 833_CR43 publication-title: J Neuroeng Rehabil. – volume: 19 start-page: 260 issue: 3 year: 2011 ident: 833_CR13 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2011.2108667 – volume: 22 start-page: 810 issue: 4 year: 2014 ident: 833_CR51 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2014.2306000 – year: 2018 ident: 833_CR22 publication-title: Sci Robot doi: 10.1126/scirobotics.aat3630 – volume: 26 start-page: 7791 issue: 30 year: 2006 ident: 833_CR26 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.0830-06.2006 – volume: 6 start-page: 1 issue: 257 year: 2014 ident: 833_CR9 publication-title: Sci Transl Med doi: 10.1126/scitranslmed.3008933 – start-page: 481 volume-title: Geographical Information Systems, volume 1: principles and technical issues year: 1999 ident: 833_CR32 – volume: 52 start-page: 449 issue: 4 year: 2015 ident: 833_CR53 publication-title: J Rehabil Res Develop. doi: 10.1682/JRRD.2014.05.0134 – year: 2012 ident: 833_CR15 publication-title: IEEE Int Workshop MachLearn Signal Process MLSP doi: 10.1109/MLSP.2012.6349712 – volume: 22 start-page: 501 issue: 3 year: 2014 ident: 833_CR17 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2013.2278411 – volume: 22 start-page: 269 issue: 2 year: 2014 ident: 833_CR12 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2014.2305520 – year: 2015 ident: 833_CR25 publication-title: Int IEEE/EMBS Conf Neural Eng NER. doi: 10.1109/NER.2015.7146707 – volume: 15 start-page: 111 issue: 1 year: 2007 ident: 833_CR11 publication-title: IEEE Trans Neural Syst Rehabil Eng doi: 10.1109/TNSRE.2007.891391 – year: 2015 ident: 833_CR2 publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2014.07.016 – ident: 833_CR35 doi: 10.1109/EMBC.2012.6346734 – year: 2018 ident: 833_CR7 publication-title: Sci Transl Med. doi: 10.1126/scitranslmed.aao6990 – ident: 833_CR44 doi: 10.1371/journal.pone.0186318 – volume: 54 start-page: 847 year: 2007 ident: 833_CR46 publication-title: IEEE Trans Biomed Eng. doi: 10.1109/TBME.2006.889192 – volume: 2 start-page: 1 issue: 3 year: 2014 ident: 833_CR1 publication-title: Curr Surg Rep doi: 10.1007/s40137-013-0044-8 – volume: 9 start-page: 42 issue: 1 year: 2012 ident: 833_CR21 publication-title: J NeuroEng Rehabil doi: 10.1186/1743-0003-9-42 – volume: 39 start-page: 1316 issue: 6 year: 2009 ident: 833_CR52 publication-title: IEEE Trans Syst Man Cybern Part A Syst Hum doi: 10.1109/TSMCA.2009.2028239 – volume: 51 start-page: 15 issue: 1 year: 2014 ident: 833_CR4 publication-title: J Rehabil Res Dev doi: 10.1682/JRRD.2013.02.0056 – year: 2014 ident: 833_CR8 publication-title: Sci Transl Med doi: 10.1126/scitranslmed.3008669 – volume: 12 start-page: 407 year: 2002 ident: 833_CR42 publication-title: J Electromyogr Kinesiol. doi: 10.1016/S1050-6411(02)00026-3 – volume: 103 start-page: 1532 issue: 3 year: 2010 ident: 833_CR37 publication-title: J Neurophysiol doi: 10.1152/jn.00559.2009 – volume: 16 start-page: 147 issue: 1 year: 2019 ident: 833_CR10 publication-title: J Neuroeng Rehab doi: 10.1186/s12984-019-0607-8 – volume: 3 start-page: 324 issue: 4 year: 1995 ident: 833_CR40 publication-title: IEEE Trans Rehabil Eng doi: 10.1109/86.481972 – volume: 2011 start-page: 761 year: 2011 ident: 833_CR16 publication-title: IEEE Int Conf Mechatron Autom doi: 10.1109/ICMA.2011.5985757 – volume: 56 start-page: 1070 issue: 4 year: 2009 ident: 833_CR23 publication-title: IEEE Trans Bio-Med Eng doi: 10.1109/TBME.2008.2007967 – ident: 833_CR39 – volume: 63 start-page: 737 issue: 4 year: 2016 ident: 833_CR30 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2015.2469741 – year: 1996 ident: 833_CR3 publication-title: J Prosthetics Orthotics doi: 10.1097/00008526-199601000-00003 – volume: 11 start-page: 1 issue: 9 year: 2016 ident: 833_CR31 publication-title: PLoS ONE doi: 10.1371/journal.pone.0161678 – volume: 95 start-page: 1201 issue: 6 year: 2014 ident: 833_CR34 publication-title: Arch Phys Med Rehabil. doi: 10.1016/j.apmr.2014.01.028 – volume: 12 start-page: 1 issue: 1 year: 2018 ident: 833_CR55 publication-title: Front Hum Neurosci. doi: 10.3389/fnhum.2018.00352 – volume: 9 start-page: 40 issue: 1 year: 2012 ident: 833_CR27 publication-title: J NeuroEng Rehabil doi: 10.1186/1743-0003-9-40 – volume: 12 start-page: 407 year: 2002 ident: 833_CR49 publication-title: J. Electromyogr Kinesiol. doi: 10.1016/S1050-6411(02)00026-3 – ident: 833_CR29 – volume: 18 start-page: 397 issue: 4 year: 2013 ident: 833_CR41 publication-title: J Motor Behav. doi: 10.1080/00222895.1986.10735388 |
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| Snippet | Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward... Background Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available... Background Current commercial prosthetic hand controllers limit patients’ ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available... Abstract Background Current commercial prosthetic hand controllers limit patients’ ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands.... |
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| SubjectTerms | Amputees - rehabilitation Arm - innervation Artificial arms Artificial Limbs Brain-Computer Interfaces Channels Computer applications Controllers Data collection Degrees of freedom Electrodes Electrodes, Implanted Electromyography Electromyography - methods Feedforward control Feedforward control systems Hand Hands Humans Hypotheses Interpolation Linear Models Male Matching Mechanical properties Movement Muscle, Skeletal - innervation Muscles Nearest-neighbor Patient Education as Topic - methods Physical Therapy Modalities - instrumentation Posture Prostheses Prosthetic control Regression analysis Retraining Simulation Training - methods Software State vectors Training Virtual Reality |
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| Title | Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series |
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