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Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems

机译:基于模型的无标记和基于标记运动分析系统的人类运动学重建方法

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摘要

Modeling tools related to the musculoskeletal system have been previously developed. However, the integration of the real underlying functional joint behavior is lacking and therefore available kinematic models do not reasonably replicate individual human motion. In order to improve our understanding of the relationships between muscle behavior, i.e. excursion and motion data, modeling tools must guarantee that the model of joint kinematics is correctly validated to ensure meaningful muscle behavior interpretation. This paper presents a model-based method that allows fusing accurate joint kinematic information with motion analysis data collected using either marker-based stereophotogrammetry (MBS) (i.e. bone displacement collected from reflective markers fixed on the subject's skin) or markerless single-camera (MLS) hardware. This paper describes a model-based approach (MBA) for human motion data reconstruction by a scalable registration method for combining joint physiological kinematics with limb segment poses. The presented results and kinematics analysis show that model-based MBS and MLS methods lead to physiologically-acceptable human kinematics. The proposed method is therefore available for further exploitation of the underlying model that can then be used for further modeling, the quality of which will depend on the underlying kinematic model.
机译:以前已经开发出与肌肉骨骼系统相关的建模工具。然而,缺乏真正的潜在功能联合行为的整合,因此可用的运动模型不合理地复制个体人类运动。为了改善我们对肌肉行为之间的关系的理解,即偏移和运动数据,建模工具必须保证正确验证了关节运动学模型,以确保有意义的肌肉行为解释。本文介绍了一种基于模型的方法,允许使用使用基于标记的立体术(MBS)收集的运动分析数据来融合精确的关节运动信息(即从受试者皮肤上固定的反射标记收集的骨位移)或无标记单摄像头(MLS ) 硬件。本文通过可扩展的登记方法描述了一种基于模型的方法(MBA),用于将带有肢体段姿势的联合生理运动学组合的可扩展配准方法。呈现的结果和运动学分析表明,基于模型的MBS和MLS方法导致生理上可接受的人类运动学。因此,所提出的方法可用于进一步利用可以用于进一步建模的底层模型,其质量将取决于底层运动模型。

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