Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter,and embedding a soft tissue artefact model |
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| Institution: | 1. LISSI, University of Paris-Est-Créteil, France;2. Univ Lyon, Université Claude Bernard Lyon 1, IFSTTAR, UMR_T9406, LBMC, F69622 Lyon, France;3. Department of Movement, Human and Health Sciences, Università degli Studi di Roma “Foro Italico”, Rome, Italy;4. Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Università degli Studi di Roma “Foro Italico”, Rome, Italy;5. Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Japan;1. Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT 84108, USA;2. Department of Orthopaedics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;3. Department of Bioengineering, University of Utah, 36 S. Wasatch Drive, Room 3100, Salt Lake City, UT 84112, USA;4. Department of Physical Therapy, University of Utah, 520 Wakara Way, Suite 240, Salt Lake City, UT 84108, USA;5. Scientific Computing and Imaging Institute, 72 S Central Campus Drive, Room 3750, Salt Lake City, UT 84112, USA;1. Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain;2. CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain;3. Centro de Investigación en Ingeniería Mecánica, Universitat Politècnica de València, Valencia, Spain;1. CIC INSERM 1432, Plateforme d''Investigation Technologique, CHU Dijon, France;2. Univ Lyon, Université Claude Bernard Lyon 1, IFSTTAR, LBMC UMR_T9406, F69622 Lyon, France;3. CNRFR – Rehazenter, Laboratoire d’Analyse du Mouvement et de la Posture, 1 rue André Vésale, L-2674 Luxembourg, Luxembourg;4. Laboratoire de simulation et de modélisation du mouvement, Département de kinésiologie, Université de Montréal, 1700, rue Jacques Tétreault, Laval, QC H7N 0B6, Canada;5. Research Center, Sainte-Justine Hospital, 3175 Côte-Ste-Catherine, Montreal, Quebec H3T 1C5, Canada;1. Department of Kinesiology, Université de Montréal, Montreal, QC, Canada H3C 3J7;2. CIC INSERM 1432, Plateforme d’Investigation Technologique, CHU Dijon, France;3. Univ Lyon, Université Lyon 1, IFSTTAR, LBMC UMR_T9406, F69622, Lyon, France;4. Karolinska Institutet, Stockholm, Sweden;1. Department of Mechanical Engineering, Technion Israel Institute of Technology, 32000 Haifa, Israel;2. Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy;3. Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal system, University of Rome “Foro Italico”, 00135 Rome, Italy;4. POLCOMING Department, Information Engineering Unit, University of Sassari, 07100 Sassari, Italy;5. Dept. of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;6. Laboratoire de recherche en Imagerie et Orthopédie, École de Technologie Supérieure, H3C 1K3 Montréal, Canada;7. Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;1. Laboratoire de Simulation et Modélisation du Mouvement, Département de Kinésiologie, Université de Montréal, Laval, QC, Canada;2. Karolinska Institutet and Swedish School of Sport and Health Sciences, Stockholm, Sweden;3. Université de Poitiers, Institut Pprime, UPR 3346, CNRS Bvd M&PCurie, BP30179, Futuroscope Cedex 86962, France |
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| Abstract: | To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6 mm and from 4.3 to 1.9 mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1 deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7 deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate. |
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| Keywords: | Soft-tissue-artefact Extended Kalman filter Multibody optimisation method Inverse kinematics |
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