Rigid body pose is commonly represented as the rigid body transformation from one (often reference) pose to another This is usually computed for each frame of data without any assumptions or restrictions on the temporal change of the pose. The most common algorithm was proposed by Söderkvist and Wedin (1993, "Determining the Movements of the Skeleton Using Well-configured Markers," J. Biomech., 26, pp. 1473-1477), and implies the assumption that measurement errors are isotropic and homogenous. This paper describes an alternative method based on a state space formulation and the application of an extended Kalman filter (EKF). State space models are formulated, which describe the kinematics of the rigid body. The state vector consists of six generalized coordinates (corresponding to the 6 degrees of freedom), and their first time derivatives. The state space models have linear dynamics, while the measurement function is a non-linear relation between the state vector and the observations (marker positions). An analytical expression for the linearized measurement function is derived. Tracking the rigid body motion using an EKF enables the use of a priori information on the measurement noise and type of motion to tune the filter. The EKF is time variant, which allows for a natural way of handling temporarily missing marker data. State updates are based on all the information available at each time step, even when data from fewer than three markers are available. Comparison with the method of Söderkvist and Wedin on simulated data showed a considerable improvement in accuracy with the proposed EKF method when marker data was temporarily missing. The proposed method offers an improvement in accuracy of rigid body pose estimation by incorporating knowledge of the characteristics of the movement and the measurement errors. Analytical expressions for the linearized system equations are provided, which eliminate the need for approximate discrete differentiation and which facilitate a fast implementation.
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http://dx.doi.org/10.1115/1.1894371 | DOI Listing |
Life (Basel)
December 2024
Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Harvard Medical School, 1575 Cambridge Street, Cambridge, MA 02115, USA.
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Cancers (Basel)
January 2025
Department of Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan.
Esophageal cancer (EC) is one of the leading causes of cancer-related deaths globally. Surgery is the standard treatment for resectable EC after preoperative chemoradiotherapy or chemotherapy, followed by postoperative adjuvant chemotherapy in certain cases. Upper gastrointestinal endoscopy and computed tomography (CT) are predominantly performed to evaluate the efficacy of these treatments, but their sensitivity and accuracy for evaluating minimal residual disease remain unsatisfactory, thereby requiring the development of alternative methods.
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January 2025
Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.
Background: Motion correction methods based on slice-to-volume registration (SVR) for fetal magnetic resonance imaging (MRI) allow reconstruction of three-dimensional (3-D) isotropic images of the fetal brain and body. However, all existing SVR methods are confined to research settings, which limits clinical integration. Furthermore, there have been no reported SVR solutions for low-field 0.
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January 2025
From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London SW3 6JJ, England (J.D.S., L.K., L.P., J.M., N.K., D.M.K., E.J.); Institute of Cancer Research, London, England (N.P., D.M.K.); and Department of Radiology and Nuclear Medicine, Rijnstate Hospital, Arnhem, the Netherlands (W.O.).
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January 2025
Department of Neurology, Keck School of Medicine at the University of Southern California, Los Angeles, California, USA.
Background: The neuropathologies of Alzheimer's disease (AD) and Lewy body disease (LBD) commonly co-occur. Parkinsonism is the hallmark feature in LBD but it can be difficult to predict the presence of these co-pathologies early in the course of clinical disease. Timely diagnosis has crucial implications, especially with the advent of disease-modifying therapies.
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