Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters
Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman...
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| Autor*in: |
Menegaldo, Luciano L. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Biological cybernetics - Springer Berlin Heidelberg, 1975, 111(2017), 5-6 vom: 01. Aug., Seite 335-346 |
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| Übergeordnetes Werk: |
volume:111 ; year:2017 ; number:5-6 ; day:01 ; month:08 ; pages:335-346 |
| Links: |
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| DOI / URN: |
10.1007/s00422-017-0724-z |
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| 520 | |a Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. | ||
| 650 | 4 | |a Muscle biomechanics | |
| 650 | 4 | |a Kalman filters | |
| 650 | 4 | |a EMG-driven models | |
| 650 | 4 | |a Myolectric control | |
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10.1007/s00422-017-0724-z doi (DE-627)OLC2052712070 (DE-He213)s00422-017-0724-z-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Menegaldo, Luciano L. verfasserin aut Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. Muscle biomechanics Kalman filters EMG-driven models Myolectric control Enthalten in Biological cybernetics Springer Berlin Heidelberg, 1975 111(2017), 5-6 vom: 01. Aug., Seite 335-346 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:111 year:2017 number:5-6 day:01 month:08 pages:335-346 https://doi.org/10.1007/s00422-017-0724-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_70 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2409 GBV_ILN_4012 GBV_ILN_4277 GBV_ILN_4307 AR 111 2017 5-6 01 08 335-346 |
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10.1007/s00422-017-0724-z doi (DE-627)OLC2052712070 (DE-He213)s00422-017-0724-z-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Menegaldo, Luciano L. verfasserin aut Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. Muscle biomechanics Kalman filters EMG-driven models Myolectric control Enthalten in Biological cybernetics Springer Berlin Heidelberg, 1975 111(2017), 5-6 vom: 01. Aug., Seite 335-346 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:111 year:2017 number:5-6 day:01 month:08 pages:335-346 https://doi.org/10.1007/s00422-017-0724-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_70 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2409 GBV_ILN_4012 GBV_ILN_4277 GBV_ILN_4307 AR 111 2017 5-6 01 08 335-346 |
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10.1007/s00422-017-0724-z doi (DE-627)OLC2052712070 (DE-He213)s00422-017-0724-z-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Menegaldo, Luciano L. verfasserin aut Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. Muscle biomechanics Kalman filters EMG-driven models Myolectric control Enthalten in Biological cybernetics Springer Berlin Heidelberg, 1975 111(2017), 5-6 vom: 01. Aug., Seite 335-346 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:111 year:2017 number:5-6 day:01 month:08 pages:335-346 https://doi.org/10.1007/s00422-017-0724-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_70 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2409 GBV_ILN_4012 GBV_ILN_4277 GBV_ILN_4307 AR 111 2017 5-6 01 08 335-346 |
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10.1007/s00422-017-0724-z doi (DE-627)OLC2052712070 (DE-He213)s00422-017-0724-z-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Menegaldo, Luciano L. verfasserin aut Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. Muscle biomechanics Kalman filters EMG-driven models Myolectric control Enthalten in Biological cybernetics Springer Berlin Heidelberg, 1975 111(2017), 5-6 vom: 01. Aug., Seite 335-346 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:111 year:2017 number:5-6 day:01 month:08 pages:335-346 https://doi.org/10.1007/s00422-017-0724-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_70 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2409 GBV_ILN_4012 GBV_ILN_4277 GBV_ILN_4307 AR 111 2017 5-6 01 08 335-346 |
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10.1007/s00422-017-0724-z doi (DE-627)OLC2052712070 (DE-He213)s00422-017-0724-z-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Menegaldo, Luciano L. verfasserin aut Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. Muscle biomechanics Kalman filters EMG-driven models Myolectric control Enthalten in Biological cybernetics Springer Berlin Heidelberg, 1975 111(2017), 5-6 vom: 01. Aug., Seite 335-346 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:111 year:2017 number:5-6 day:01 month:08 pages:335-346 https://doi.org/10.1007/s00422-017-0724-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_70 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2409 GBV_ILN_4012 GBV_ILN_4277 GBV_ILN_4307 AR 111 2017 5-6 01 08 335-346 |
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Menegaldo, Luciano L. |
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Menegaldo, Luciano L. ddc 570 ssgn 12 fid BIODIV misc Muscle biomechanics misc Kalman filters misc EMG-driven models misc Myolectric control Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters |
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real-time muscle state estimation from emg signals during isometric contractions using kalman filters |
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Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters |
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Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. © Springer-Verlag GmbH Germany 2017 |
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Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. © Springer-Verlag GmbH Germany 2017 |
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Abstract State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17$$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. © Springer-Verlag GmbH Germany 2017 |
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