Musculoskeletal Inverse Kinematics Tool for Inertial Motion Capture Data Based on the Adaptive Unscented Kalman Smoother: An Implementation for OpenSim
Purpose Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT—a tool for whole-body kinematics estimation in the Bayesian fr...
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| Published in: | Annals of biomedical engineering Vol. 53; no. 11; pp. 2966 - 2982 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer International Publishing
01.11.2025
Springer Nature B.V |
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| ISSN: | 0090-6964, 1573-9686, 1573-9686 |
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| Abstract | Purpose
Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT—a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings.
Methods
We implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim.
Results
AUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8–1.9%), the velocities of six joints (0.7-
-
7.6%), and the accelerations of seven joints (3.0–13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3–7.6%), and the velocities of three joints (4.4–8.3%).
Conclusion
With respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator. |
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| AbstractList | PurposeConventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT—a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings.MethodsWe implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim.ResultsAUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8–1.9%), the velocities of six joints (0.7--7.6%), and the accelerations of seven joints (3.0–13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3–7.6%), and the velocities of three joints (4.4–8.3%).ConclusionWith respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator. Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT-a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings.PURPOSEConventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT-a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings.We implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim.METHODSWe implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim.AUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8-1.9%), the velocities of six joints (0.7- - 7.6%), and the accelerations of seven joints (3.0-13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3-7.6%), and the velocities of three joints (4.4-8.3%).RESULTSAUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8-1.9%), the velocities of six joints (0.7- - 7.6%), and the accelerations of seven joints (3.0-13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3-7.6%), and the velocities of three joints (4.4-8.3%).With respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator.CONCLUSIONWith respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator. Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT-a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings. We implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim. AUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8-1.9%), the velocities of six joints (0.7- 7.6%), and the accelerations of seven joints (3.0-13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3-7.6%), and the velocities of three joints (4.4-8.3%). With respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator. Purpose Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no estimate for the uncertainty of their solutions. This paper presents AUKSMIKT—a tool for whole-body kinematics estimation in the Bayesian framework to account for these shortcomings. Methods We implemented AUKSMIKT as a C++ class that extends the OpenSim (v4.5) application programming interface. AUKSMIKT is based on the unscented Kalman filter combined with a run-time estimator of process and observation noises, and a fixed-lag Rauch-Tung-Striebel smoother. We tested the performance of AUKSMIKT using data from a public dataset consisting of both optical and inertial motion capture data recorded from overground walking subjects. We computed the mean absolute errors of estimated angular positions, velocities, and accelerations with respect to the gold standard optical motion capture estimates, and compared these metrics to those obtained from the least squares estimation-based tool native to OpenSim. Results AUKSMIKT produced smaller errors than the native tool for the angular position of three joints (0.8–1.9%), the velocities of six joints (0.7- - 7.6%), and the accelerations of seven joints (3.0–13.7%). AUKSMIKT produced larger errors in the angular positions of five joints (1.3–7.6%), and the velocities of three joints (4.4–8.3%). Conclusion With respect to the optical motion capture solution, AUKSMIKT can estimate lower-body kinematics from inertial motion capture data with comparable or higher accuracy than the native OpenSim least squares estimator. |
| Author | Karjalainen, Pasi A. Vartiainen, Paavo Kortelainen, Matti J. Lavikainen, Jere Beattie, Alexander |
| Author_xml | – sequence: 1 givenname: Matti J. orcidid: 0000-0002-6873-0531 surname: Kortelainen fullname: Kortelainen, Matti J. email: matti.kortelainen@uef.fi organization: Department of Technical Physics, University of Eastern Finland – sequence: 2 givenname: Paavo orcidid: 0000-0003-0974-0913 surname: Vartiainen fullname: Vartiainen, Paavo organization: Department of Technical Physics, University of Eastern Finland – sequence: 3 givenname: Alexander orcidid: 0009-0005-7639-1102 surname: Beattie fullname: Beattie, Alexander organization: Department of Technical Physics, University of Eastern Finland – sequence: 4 givenname: Jere orcidid: 0000-0002-7636-8229 surname: Lavikainen fullname: Lavikainen, Jere organization: Department of Technical Physics, University of Eastern Finland, PSHVA Research Services, Kuopio University Hospital, Wellbeing Services County of North Savo – sequence: 5 givenname: Pasi A. orcidid: 0000-0002-1267-493X surname: Karjalainen fullname: Karjalainen, Pasi A. organization: Department of Technical Physics, University of Eastern Finland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40721566$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TAC.1972.1100100 10.1109/ACC.2005.1470611 10.1016/j.gaitpost.2014.10.025 10.1186/s12984-022-01001-x 10.1016/S0966-6362(96)01088-0 10.1007/s11044-023-09938-0 10.1109/TBME.2016.2523512 10.1016/j.compchemeng.2009.01.012 10.1080/00140139.2015.1079335 10.5281/zenodo.10559504 10.1145/195784.197480 10.1109/TBME.2022.3156018 10.2514/3.3166 10.1016/j.gaitpost.2004.05.002 10.1007/s10439-014-1181-7 10.1186/s12891-019-2416-4 10.1038/s41598-025-89716-4 10.1016/j.gaitpost.2020.10.026 10.1007/978-3-031-23824-6 10.1109/TBME.2016.2586891 10.1016/j.jbiomech.2008.09.035 10.1016/j.jbiomech.2014.12.049 10.1002/0470045345 10.1016/j.ymssp.2020.106837 10.1109/ICIEA.2009.5138274 10.1109/TBME.2014.2318354 10.1016/j.jelekin.2018.06.009 10.1016/j.gaitpost.2007.07.012 10.1016/j.gaitpost.2012.01.016 10.3233/NRE-130928 10.3109/03093646.2010.485597 10.3390/s21051804 10.1109/ASSPCC.2000.882463 10.1016/j.jbiomech.2016.12.027 10.1371/journal.pcbi.1006223 10.1016/j.ymssp.2019.07.021 10.1016/j.mechmachtheory.2012.07.010 10.1016/j.pmr.2018.12.012 10.1016/0141-1195(86)90098-7 10.1145/3272127.3275108 10.1049/iet-spr.2012.0330 10.1016/j.dib.2024.110841 10.1109/JSEN.2022.3225931 10.1017/CBO9781139344203 10.1016/j.jbiomech.2015.09.021 10.1371/journal.pone.0276302 10.1016/j.jelekin.2017.09.004 10.1016/j.bspc.2007.09.001 10.1080/00207728908910103 10.1109/TBME.2015.2403368 10.3389/fbioe.2022.874725 10.1016/j.gaitpost.2021.10.024 10.1007/978-3-319-39904-1_31 10.1109/JPROC.2003.823141 10.1109/TAC.2008.919531 10.1038/s41598-023-31906-z 10.1152/jn.01042.2012 |
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| References | D Calvetti (3807_CR8) 2023 3807_CR38 C Pizzolato (3807_CR6) 2015; 48 M Zago (3807_CR17) 2018; 42 U Lugrís (3807_CR51) 2023; 60 RM Asl (3807_CR24) 2019; 132 Y Huang (3807_CR41) 2016; 63 RA Resende (3807_CR49) 2015; 41 TK Uchida (3807_CR59) 2022; 10 JC Hart (3807_CR29) 1994; 13 OA Kannape (3807_CR55) 2013; 110 S Kolås (3807_CR63) 2009; 33 J Lavikainen (3807_CR32) 2024; 56 D Simon (3807_CR20) 2006 F De Groote (3807_CR9) 2008; 41 Y Huang (3807_CR44) 2018; 37 R Zügner (3807_CR18) 2019; 20 BC Conner (3807_CR3) 2022; 91 E Owen (3807_CR42) 2010; 34 L Chang (3807_CR54) 2013; 7 3807_CR47 S Särkkä (3807_CR11) 2013 S Särkkä (3807_CR26) 2008; 53 R Riemer (3807_CR57) 2008; 27 D Stanev (3807_CR56) 2021; 21 V Hernandez (3807_CR43) 2021; 83 R Pàmies-Vilà (3807_CR58) 2012; 58 3807_CR52 SM Moghadam (3807_CR46) 2023; 13 HJ Woltring (3807_CR36) 1986; 8 A Leardini (3807_CR40) 2005; 21 P Vartiainen (3807_CR12) 2014; 61 M Al Borno (3807_CR37) 2022; 19 V Bonnet (3807_CR13) 2017; 62 3807_CR19 A Seth (3807_CR5) 2018; 14 A Rajagopal (3807_CR33) 2016; 63 J Lavikainen (3807_CR31) 2024 ZF Lerner (3807_CR34) 2015; 48 K Park (3807_CR48) 2012; 36 SJ Julier (3807_CR53) 2004; 92 M Cortes (3807_CR4) 2013; 33 MC Schall Jr (3807_CR16) 2016; 59 3807_CR62 M Song (3807_CR22) 2020; 143 3807_CR61 3807_CR60 3807_CR23 RK Mehra (3807_CR21) 1972; 17 A Esquenazi (3807_CR2) 2019; 30 B Hur (3807_CR45) 2025; 15 A Esrafilian (3807_CR35) 2022; 69 S Fioretti (3807_CR27) 1989; 20 H Zhou (3807_CR1) 2007; 3 E Bernardes (3807_CR28) 2022; 17 M El-Gohary (3807_CR15) 2015; 62 Y Koshino (3807_CR50) 2017; 37 CA Myers (3807_CR7) 2014; 43 3807_CR30 JP Holden (3807_CR39) 1997; 5 HE Rauch (3807_CR10) 1965; 3 L Truppa (3807_CR14) 2023; 23 YS Shmaliy (3807_CR25) 2017; 37 |
| References_xml | – volume: 17 start-page: 693 issue: 5 year: 1972 ident: 3807_CR21 publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.1972.1100100 – ident: 3807_CR52 doi: 10.1109/ACC.2005.1470611 – volume: 41 start-page: 395 issue: 2 year: 2015 ident: 3807_CR49 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2014.10.025 – volume: 19 start-page: 22, 1–11 year: 2022 ident: 3807_CR37 publication-title: J. NeuroEng. Rehabil. doi: 10.1186/s12984-022-01001-x – volume: 5 start-page: 217 issue: 3 year: 1997 ident: 3807_CR39 publication-title: Gait & Posture doi: 10.1016/S0966-6362(96)01088-0 – volume: 60 start-page: 3 issue: 1 year: 2023 ident: 3807_CR51 publication-title: Multibody Syst.Dyn. doi: 10.1007/s11044-023-09938-0 – volume: 63 start-page: 2278 issue: 11 year: 2016 ident: 3807_CR41 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2016.2523512 – ident: 3807_CR61 – volume: 33 start-page: 1386 issue: 8 year: 2009 ident: 3807_CR63 publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2009.01.012 – volume: 59 start-page: 591 year: 2016 ident: 3807_CR16 publication-title: Ergonomics doi: 10.1080/00140139.2015.1079335 – year: 2024 ident: 3807_CR31 publication-title: Zenodo doi: 10.5281/zenodo.10559504 – volume: 13 start-page: 256 year: 1994 ident: 3807_CR29 publication-title: ACM Trans. Graph. doi: 10.1145/195784.197480 – volume: 69 start-page: 2860 year: 2022 ident: 3807_CR35 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2022.3156018 – volume: 37 start-page: 70 year: 2017 ident: 3807_CR25 publication-title: IEEE Control Syst. Mag. – volume: 3 start-page: 1445 year: 1965 ident: 3807_CR10 publication-title: AIAA J. doi: 10.2514/3.3166 – volume: 21 start-page: 212 issue: 2 year: 2005 ident: 3807_CR40 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2004.05.002 – volume: 43 start-page: 1098 year: 2014 ident: 3807_CR7 publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-014-1181-7 – volume: 20 start-page: 1 year: 2019 ident: 3807_CR18 publication-title: BMC Musculoskelet. Disord. doi: 10.1186/s12891-019-2416-4 – volume: 15 start-page: 5287 issue: 1 year: 2025 ident: 3807_CR45 publication-title: Sci. Rep. doi: 10.1038/s41598-025-89716-4 – ident: 3807_CR62 – volume: 83 start-page: 185 year: 2021 ident: 3807_CR43 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2020.10.026 – volume-title: Bayesian Scientific Computing year: 2023 ident: 3807_CR8 doi: 10.1007/978-3-031-23824-6 – volume: 63 start-page: 2068 issue: 10 year: 2016 ident: 3807_CR33 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2016.2586891 – volume: 41 start-page: 3390 year: 2008 ident: 3807_CR9 publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2008.09.035 – volume: 48 start-page: 644 issue: 4 year: 2015 ident: 3807_CR34 publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2014.12.049 – volume-title: Optimal State Estimation: Kalman, $$\text{H}_{\infty }$$, and Nonlinear Approaches year: 2006 ident: 3807_CR20 doi: 10.1002/0470045345 – volume: 143 year: 2020 ident: 3807_CR22 publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2020.106837 – ident: 3807_CR23 doi: 10.1109/ICIEA.2009.5138274 – volume: 61 start-page: 2167 year: 2014 ident: 3807_CR12 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2318354 – volume: 42 start-page: 44 year: 2018 ident: 3807_CR17 publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2018.06.009 – volume: 27 start-page: 578 issue: 4 year: 2008 ident: 3807_CR57 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2007.07.012 – volume: 36 start-page: 120 issue: 1 year: 2012 ident: 3807_CR48 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2012.01.016 – volume: 33 start-page: 57 year: 2013 ident: 3807_CR4 publication-title: NeuroRehabilitation doi: 10.3233/NRE-130928 – volume: 34 start-page: 254 issue: 3 year: 2010 ident: 3807_CR42 publication-title: Prosthet. Orthot. Int. doi: 10.3109/03093646.2010.485597 – volume: 21 start-page: 1804 issue: 5 year: 2021 ident: 3807_CR56 publication-title: Sensors doi: 10.3390/s21051804 – ident: 3807_CR19 doi: 10.1109/ASSPCC.2000.882463 – volume: 62 start-page: 140 year: 2017 ident: 3807_CR13 publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2016.12.027 – volume: 14 start-page: 1 year: 2018 ident: 3807_CR5 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1006223 – volume: 132 start-page: 670 year: 2019 ident: 3807_CR24 publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.07.021 – volume: 58 start-page: 153 year: 2012 ident: 3807_CR58 publication-title: Mech. Mach. Theory doi: 10.1016/j.mechmachtheory.2012.07.010 – volume: 30 start-page: 385 year: 2019 ident: 3807_CR2 publication-title: Phys. Med. Rehabil. Clin. N. Am. doi: 10.1016/j.pmr.2018.12.012 – volume: 8 start-page: 104 year: 1986 ident: 3807_CR36 publication-title: Adv. Eng. Softw. doi: 10.1016/0141-1195(86)90098-7 – volume: 37 start-page: 1 issue: 6 year: 2018 ident: 3807_CR44 publication-title: ACM Trans. Graph. doi: 10.1145/3272127.3275108 – volume: 7 start-page: 167 issue: 3 year: 2013 ident: 3807_CR54 publication-title: IET Signal Proc. doi: 10.1049/iet-spr.2012.0330 – ident: 3807_CR30 – volume: 56 start-page: 110841 year: 2024 ident: 3807_CR32 publication-title: Data Brief doi: 10.1016/j.dib.2024.110841 – volume: 23 start-page: 3212 year: 2023 ident: 3807_CR14 publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3225931 – ident: 3807_CR38 – volume-title: Bayesian Filtering and Smoothing year: 2013 ident: 3807_CR11 doi: 10.1017/CBO9781139344203 – volume: 48 start-page: 3929 year: 2015 ident: 3807_CR6 publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2015.09.021 – volume: 17 start-page: e0276302, 1–13 issue: 11 year: 2022 ident: 3807_CR28 publication-title: PLoS ONE doi: 10.1371/journal.pone.0276302 – volume: 37 start-page: 75 year: 2017 ident: 3807_CR50 publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/j.jelekin.2017.09.004 – volume: 3 start-page: 1 year: 2007 ident: 3807_CR1 publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2007.09.001 – volume: 20 start-page: 33 year: 1989 ident: 3807_CR27 publication-title: Int. J. Syst. Sci. doi: 10.1080/00207728908910103 – volume: 62 start-page: 1759 issue: 7 year: 2015 ident: 3807_CR15 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2015.2403368 – volume: 10 start-page: 1 year: 2022 ident: 3807_CR59 publication-title: Front. Bioeng. Biotechnol. doi: 10.3389/fbioe.2022.874725 – volume: 91 start-page: 165 year: 2022 ident: 3807_CR3 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2021.10.024 – ident: 3807_CR47 – ident: 3807_CR60 doi: 10.1007/978-3-319-39904-1_31 – volume: 92 start-page: 401 year: 2004 ident: 3807_CR53 publication-title: Proc. IEEE doi: 10.1109/JPROC.2003.823141 – volume: 53 start-page: 845 year: 2008 ident: 3807_CR26 publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2008.919531 – volume: 13 start-page: 5046 issue: 1 year: 2023 ident: 3807_CR46 publication-title: Sci. Rep. doi: 10.1038/s41598-023-31906-z – volume: 110 start-page: 1837 year: 2013 ident: 3807_CR55 publication-title: J. Neurophysiol. doi: 10.1152/jn.01042.2012 |
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Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no... Conventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no... PurposeConventional tools for human kinematics estimation presume that observations are subject to uncorrelated, zero-mean Gaussian noise, and they provide no... |
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| SubjectTerms | Adult Algorithms Angular position Angular velocity Application programming interface Bayes Theorem Bayesian analysis Biochemistry Biological and Medical Physics Biomechanical Phenomena Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Body kinematics Classical Mechanics Errors Estimates Female Humans Inverse kinematics Kalman filters Kinematics Male Models, Biological Motion Capture Open source software Original Article Random noise Software Time series Walking - physiology |
| Title | Musculoskeletal Inverse Kinematics Tool for Inertial Motion Capture Data Based on the Adaptive Unscented Kalman Smoother: An Implementation for OpenSim |
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