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手指运动学建模与实时手部运动估计

Finger kinematic modeling and real-time hand motion estimation.

作者信息

Cerveri P, De Momi E, Lopomo N, Baud-Bovy G, Barros R M L, Ferrigno G

机构信息

Bioengineering Department, Politecnico di Milano University, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy.

出版信息

Ann Biomed Eng. 2007 Nov;35(11):1989-2002. doi: 10.1007/s10439-007-9364-0. Epub 2007 Aug 15.

Abstract

This paper describes methods and experimental studies concerned with quantitative reconstruction of finger movements in real-time, by means of multi-camera system and 24 surface markers. The approach utilizes a kinematic model of the articulated hand which consists in a hierarchical chain of rigid body segments characterized by 22 functional degrees of freedom and the global roto-translation. This work is focused on the experimental evaluation of a kinematical hand model for biomechanical analysis purposes. From a static posture, a completely automatic calibration procedure, based on anthropometric measures and geometric constraints, computes axes, and centers of rotations which are then utilized as the base of an interactive real-time animation of the hand model. The motion tracking, based on automatic marker labeling and predictive filter, is empowered by introducing constraints from functional finger postures. The validation is performed on four normal subjects through different right-handed motor tasks involving voluntary flexion-extension of the thumb, voluntary abduction-adduction of the thumb, grasping, and finger pointing. Performances are tested in terms of repeatability of angular profiles, model-based ability to predict marker trajectories and tracking success during real-time motion estimation. Results show intra-subject repeatability of the model calibration both to different postures and to re-marking in the range of 0.5 and 2 mm, respectively. Kinematic estimation proves satisfactory in terms of prediction capability (index finger: maximum RMSE 2.02 mm; thumb: maximum RMSE 3.25 mm) and motion reproducibility (R (2) coefficients--index finger: 0.96, thumb: 0.94). During fast grasping sequence (60 Hz), the percentage of residual marker occlusions is less than 1% and processing and visualization frequency of 50 Hz confirms the real-time capability of the motion estimation system.

摘要

本文介绍了通过多摄像头系统和24个表面标记实时定量重建手指运动的方法和实验研究。该方法利用了关节手的运动学模型,该模型由具有22个功能自由度和全局旋转平移的刚体段的层次链组成。这项工作的重点是用于生物力学分析目的的手部运动学模型的实验评估。从静态姿势开始,基于人体测量学措施和几何约束的完全自动校准程序计算轴和旋转中心,然后将其用作手部模型交互式实时动画的基础。基于自动标记和预测滤波器的运动跟踪通过引入功能手指姿势的约束而得到增强。通过涉及拇指自愿屈伸、拇指自愿外展内收、抓握和手指指向的不同右手运动任务,对四名正常受试者进行了验证。在角度轮廓的可重复性、基于模型预测标记轨迹的能力以及实时运动估计期间的跟踪成功率方面对性能进行了测试。结果表明,模型校准在不同姿势和重新标记方面的受试者内重复性分别在0.5和2毫米范围内。运动学估计在预测能力(食指:最大均方根误差2.02毫米;拇指:最大均方根误差3.25毫米)和运动再现性(R(2)系数——食指:0.96,拇指:0.94)方面证明是令人满意的。在快速抓握序列(60赫兹)期间,残留标记遮挡的百分比小于1%,50赫兹的处理和可视化频率证实了运动估计系统的实时能力。

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