Movement-Based Control for Upper-Limb Prosthetics: Is the Regression Technique the Key to a Robust and Accurate Control?
Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use r...
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| Vydáno v: | Frontiers in neurorobotics Ročník 12; s. 41 |
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26.07.2018
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| Abstract | Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients. |
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| AbstractList | Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients. Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g. elbow) based on the motions of proximal ones (e.g. shoulder). The regression techniques, used to model the coordinations, are various (Artificial Neural Networks, Principal Components Analysis, etc.) and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR) and Principal Components Analysis (PCA), RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients. Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients.Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients. |
| Author | Roby-Brami, Agnès Legrand, Mathilde de Montalivet, Etienne Jarrassé, Nathanaël Merad, Manelle |
| AuthorAffiliation | Sorbonne Université, CNRS, INSERM, Institut des Systèmes Intelligents et de Robotique, International Society for Intelligence Research (ISIR) , Paris , France |
| AuthorAffiliation_xml | – name: Sorbonne Université, CNRS, INSERM, Institut des Systèmes Intelligents et de Robotique, International Society for Intelligence Research (ISIR) , Paris , France |
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| CitedBy_id | crossref_primary_10_1080_10255842_2020_1714976 crossref_primary_10_1109_TNSRE_2023_3258225 crossref_primary_10_1109_TCYB_2019_2920376 crossref_primary_10_1109_TNSRE_2020_3036320 crossref_primary_10_1016_j_jor_2020_12_009 crossref_primary_10_1038_s42256_024_00825_7 crossref_primary_10_3390_biomimetics9090532 crossref_primary_10_1177_00187208251367179 crossref_primary_10_3389_fnbot_2023_1084000 crossref_primary_10_1038_s41598_021_00376_6 |
| Cites_doi | 10.1109/86.736154 10.1016/S0021-9290(01)00222-6 10.1016/j.apmr.2012.03.011 10.1523/JNEUROSCI.01-07-00710.1981 10.1007/978-3-319-46669-9_80 10.1016/j.humov.2009.03.003 10.1016/j.clinbiomech.2005.06.004 10.3389/fnins.2016.00209 10.1109/TBME.1982.324954 10.1109/TNSRE.2015.2483375 10.1109/JPROC.2008.922591 10.1109/51.897830 10.1523/JNEUROSCI.02-04-00399.1982 10.1109/TNSRE.2017.2693234 10.1007/s00221-003-1438-0 10.1590/S0100-879X2008005000019 10.3389/fnbot.2018.00001 10.1152/jn.2000.83.5.2661 10.1186/s12984-015-0044-2 10.1016/0006-8993(82)90410-3 10.22086/jbpe.v0i0.524 10.1109/TBME.2012.2185494 10.1177/0309364613506913 10.1016/j.neunet.2015.05.005 10.1152/jn.00189.2003 10.3389/fnbot.2014.00022 10.1016/j.neuroscience.2017.03.025 10.1109/TBME.2011.2179545 10.1109/TBME.2006.883695 10.1109/TNSRE.2005.858458 10.1109/EMBC.2015.7318892 |
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| Keywords | movement-based control shoulder-elbow coordinations upper-limb prosthetics regression algorithms motor strategy |
| Language | English |
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| References | Bockemühl (B5) 2010; 29 Kaliki (B15) 2013; 60 Merad (B20); 1 Lunardini (B19) 2016; 24 Micera (B24) 2005; 20 Lacquaniti (B17) 1982; 2 Montagnani (B26) 2015 Lacquaniti (B18) 1982; 252 Merad (B21) 2018; 12 Cirstea (B8) 2003; 151 Resnik (B31) 2013; 38 Alshammary (B1) 2016 Chu (B7) 2006; 53 Sainburg (B32) 2011; 83 Cordella (B9) 2016; 10 Popovic (B28) 2002 B30 Johannes (B13) 2011; 30 Schaffer (B34) 2017; 350 Iftime (B12) 2005; 13 Park (B27) 1998; 6 Kaliki (B14) 2008; 96 Soechting (B35) 1981; 1 Engdahl (B10) 2012; 12 Merad (B22) Metzger (B23) 2012; 93 Saridis (B33) 1982; 29 Stulp (B36) 2015; 69 Popovic (B29) 2001; 20 Bagesteiro (B2) 2003; 90 Mijovic (B25) 2008; 41 Castellini (B6) 2014; 8 Balasubramanian (B3) 2012; 59 B4 Farokhzadi (B11) 2016; 7 Kontson (B16) 2017; 25 Wu (B38) 2002; 35 Vallery (B37) 2006 |
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| SubjectTerms | Activities of daily living Automatic Biotechnology Computer Science Elbow Engineering Sciences Feasibility studies International conferences motor strategy movement-based control Neural networks Neurosciences Pattern recognition Principal components analysis Prostheses Prosthetics regression algorithms Regression analysis Robotics and AI Shoulder shoulder-elbow coordinations upper-limb prosthetics |
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| Title | Movement-Based Control for Upper-Limb Prosthetics: Is the Regression Technique the Key to a Robust and Accurate Control? |
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