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Research Papers

Robust Identification of Three-Dimensional Thumb and Index Finger Kinematics With a Minimal Set of Markers

[+] Author and Article Information
Zong-Ming Li

e-mail: liz4@ccf.org
Department of Biomedical Engineering,
Cleveland Clinic,
Cleveland, OH 44195;
Department of Orthopaedic Surgery,
Cleveland Clinic,
Cleveland, OH 44195;
Department of Physical
Medicine and Rehabilitation,
Cleveland Clinic,
Cleveland, OH 44195

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received July 17, 2012; final manuscript received May 28, 2013; accepted manuscript posted June 5, 2013; published online July 10, 2013. Assoc. Editor: Richard Neptune.

J Biomech Eng 135(9), 091002 (Jul 10, 2013) (9 pages) Paper No: BIO-12-1296; doi: 10.1115/1.4024753 History: Received July 17, 2012; Revised May 28, 2013; Accepted June 05, 2013

This study presents a methodology to determine thumb and index finger kinematics while utilizing a minimal set of markers. The motion capture of skin-surface markers presents inherent challenges for the accurate and comprehensive measurement of digit kinematics. As such, it is desirable to utilize robust methods for assessing digit kinematics with fewer markers. The approach presented in this study involved coordinate system alignment, locating joint centers of rotation, and a solution model to estimate three-dimensional (3-D) digit kinematics. The solution model for each digit was based on assumptions of rigid-body interactions, specific degrees of freedom (DOFs) at each located joint, and the aligned coordinate system definitions. Techniques of inverse kinematics and optimization were applied to calculate the 3-D position and orientation of digit segments during pinching between the thumb and index finger. The 3-D joint center locations were reliably fitted with mean coefficients of variation below 5%. A parameterized form of the solution model yielded feasible solutions that met specified tolerance and convergence criteria for over 85% of the test points. The solution results were intuitive to the pinching function. The thumb was measured to be rotated about the CMC joint to bring it into opposition to the index finger and larger rotational excursions (>10 deg) were observed in flexion/extension compared to abduction/adduction and axial rotation for all joints. While the solution model produced results similar to those computed from a full marker set, the model facilitated the usage of fewer markers, which inherently lessened the effects of passive motion error and reduced the post-experimental effort required for marker processing.

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Figures

Grahic Jump Location
Fig. 1

Marker sets employed in the current study. (a) The minimal marker set (nine markers total), (b) the intermediate marker set (17 markers total), and (c) the complete marker set (21 markers total).

Grahic Jump Location
Fig. 2

Identifying the joint centers in the distal to proximal direction. (a) General demonstration of how the joint center is identified according to the spherical motions of markers affixed to one segment relative to the reference frame on an adjacent segment. (b) Coordinate systems affixed to each digit segment and the corresponding joint centers for each digit. (Notes: the blue circles indicate the index finger joint centers and the green circles indicate the thumb joint centers; the figure of the hand was generated in Poser software (SmithMicro Inc., Watsonville, CA.)

Grahic Jump Location
Fig. 3

Digit alignment protocol. (a) Static calibration of the thumb and index finger was performed using the digit alignment device (DAD) and nail marker clusters. (b) Transformation relationships among the block coordinate system (BCS), cluster coordinate system (CCS), and virtual coordinate system (VCS) were derived. (c) In the current study, the thumb and index finger were interfaced with the digit alignment device (DAD) to align all clusters in the full marker set. (Note: figures (a) and (b) are adapted from [7].

Grahic Jump Location
Fig. 4

Dynamic pinch trial. (a) Hand in the open position to initiate (and terminate) pinch cycle. (b) Hand in the closed tip-pinch position. The tip-pinch configuration position approximated the functional grip of small-width objects, resulting in the exertion of low finger joint forces [1].

Grahic Jump Location
Fig. 5

Solution modeling of the thumb and index finger digits. (a) Presumed solution model for each digit formed a 3-segment serial-link open chain from the hand segment treated as a grounded reference. Moving proximal-to-distal, the index finger joints were assumed to have 2, 1, and 1 degrees of freedom (DOFs). The corresponding thumb joints have 3, 2, and 1 DOFs. (b) Sagittal view for the index finger showing the joint positions (Oi, where i indicates proximal, middle, or distal) and aligned coordinate system axes (x∧, y∧,z∧) affixed to the corresponding segment. (Note: lateral axes (x∧) are not shown but assumed to point into the page). (c) Parameterized index finger model was used for optimization with unknown quantities parameterized to be a function of only three unknowns (axial rotation angles θL1, θL2, θP2). (Note: the figure of the hand is from pixelperfectdigital.com)

Grahic Jump Location
Fig. 6

Plots are shown of the sample data of the solution model versus the comparison (from full marker set) values for the estimation of the index finger middle joint (PIP2) position and angles for the MCP2, PIP2, and DIP2 joints

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