Artefact-reduced kinematics measurement using a geometric finger model with mixture-prior particle filtering

It is challenging to measure the finger's kinematics of underlying bones in vivo. This paper presents a new method of finger kinematics measurement, using a geometric finger model and several markers deliberately stuck on skin surface. Using a multiple-view camera system, the optimal motion parameters of finger model were estimated using the proposed mixture-prior particle filtering. This prior, consisting of model and marker information, avoids generating improper particles for achieving near real-time performance. This method was validated using a planar fluoroscopy system that worked simultaneously with photographic system. Ten male subjects with asymptomatic hands were investigated in experiments. The results showed that the kinematic parameters could be estimated more accurately by the proposed method than by using only markers. There was 20–40% reduction in skin artefacts achieved for finger flexion/extension. Thus, this profile system can be developed as a tool of reliable kinematics measurement with good applicability for hand rehabilitation.

[1]  Scott F. M. Duncan,et al.  Biomechanics of the hand. , 2013, Hand clinics.

[2]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[3]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Wen-Yan Chang,et al.  Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering , 2008, IEEE Transactions on Image Processing.

[5]  D G Kamper,et al.  Kinetic and kinematic workspaces of the index finger following stroke. , 2005, Brain : a journal of neurology.

[6]  Peter W. Halligan,et al.  RIVCAM: a simple video-based kinematic analysis for clinical disorders of gait , 2002, Comput. Methods Programs Biomed..

[7]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[8]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[10]  H-Y Hsu,et al.  The validity of using a video-based motion analysis system for measuring maximal area of fingertip motion and angular variation , 2002, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[11]  Jie Tang,et al.  Coordination of thumb joints during opposition. , 2007, Journal of biomechanics.

[12]  Haiying Guan,et al.  Model-based 3D hand posture estimation from a single 2D image , 2002, Image Vis. Comput..

[13]  G N Malaviya,et al.  Evaluation of Methods of Claw Finger Correction Using the Finger Dynamography Technique , 1993, Journal of hand surgery.

[14]  Fong-Chin Su,et al.  Feasibility of using a video-based motion analysis system for measuring thumb kinematics. , 2002, Journal of biomechanics.

[15]  Xudong Zhang,et al.  Biodynamic modeling, system identification, and variability of multi-finger movements. , 2007, Journal of biomechanics.

[16]  William M. Stanish,et al.  Estimation of the Intraclass Correlation Coefficient for the Analysis of Covariance Model , 1983 .

[17]  Javier Ortego,et al.  Human hand descriptions and gesture recognition for object manipulation , 2010, Computer methods in biomechanics and biomedical engineering.

[18]  Laurence Cheze,et al.  A new method for motion capture of the scapula using an optoelectronic tracking device: a feasibility study , 2010, Computer methods in biomechanics and biomedical engineering.

[19]  R. B. Davis,et al.  A gait analysis data collection and reduction technique , 1991 .

[20]  M. Schwartz,et al.  A new method for estimating joint parameters from motion data. , 2004, Journal of biomechanics.

[21]  G. Ferrigno,et al.  Real-time human motion estimation using biomechanical models and non-linear state-space filters , 2006, Medical and Biological Engineering and Computing.

[22]  G Ferrigno,et al.  Kinematical models to reduce the effect of skin artifacts on marker-based human motion estimation. , 2005, Journal of biomechanics.

[23]  W. Mallon,et al.  Digital ranges of motion: normal values in young adults. , 1991, The Journal of hand surgery.

[24]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[25]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[26]  G. Ferrigno,et al.  In Vivo Validation of a Realistic Kinematic Model for the Trapezio-Metacarpal Joint Using an Optoelectronic System , 2008, Annals of Biomedical Engineering.

[27]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[28]  Ying Wu,et al.  Capturing human hand motion in image sequences , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[29]  Nicholas G. Polson,et al.  Particle Filtering , 2006 .

[30]  Fong-Chin Su,et al.  Reliable Model-based Kinematics Analysis System for Articulated Fingers , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  J Chen,et al.  The limitations of the instantaneous centre of rotation in joint research. , 1999, Journal of oral rehabilitation.

[32]  Sung Uk Lee,et al.  3D hand reconstruction from a monocular view , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[33]  T P Andriacchi,et al.  Correcting for deformation in skin-based marker systems. , 2001, Journal of biomechanics.

[34]  H. Chiu,et al.  The Motion Analysis System and The Maximal Area of Fingertip Motion , 1996, Journal of hand surgery.