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

Abstract

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.

The Envelope of Physiological Motion of the First Carpometacarpal Joint

“…However, this study advances our understanding of CMC kinematics by demonstrating that coupling of the primary motions with internal-external rotations and translation along the screw axes are continuous functions of the direction of motion. This is in contrast to other studies that have reported minimal or no coupled motions, perhaps because those studies were focused on the primary motions [1,4,[6][7][8]23,[26][27][28], or because the kinematic data were acquired using surface marker-based motion capture systems where skin motion artifact can be large enough to mask the more modest coupled motions. Coupling with internal-external rotations is evident by the nonzero value of ele, in which positive values of ele indicate coupling with internal rotations and negative values of ele indicate coupling with external rotation.…”

“…Subsequently, in a review of thumb CMC instability and dislocation, Edmunds proposed that the soft tissues surrounding the CMC joint generate a stabilizing screw-home motion at the end of thumb opposition, indicating a coupling between flexion and internal rotation [2,3]. Previous experimental studies, however, have not reported translational or a rotational coupling motions of the thumb CMC joint consistent with such a screw-home mechanism [1,[4][5][6][7][8].…”

Evaluation of Three Different Phalangeal Motion Measurement Systems and Determination of the Functional Range of Motion

“…However, this measurement method requires lengthy preparation and a complex service apparatus compared to non-optical methods. The analysis of the movements of finger joints using markers and image processing was previously investigated by Chaudhary Nataray and Zhang [6], [17], [22], but for different purposes, e.g., control of a robotic hand using image processing. These studies apply similar experimental devices as in the case of the optical device, but the aim of the measurement is different, so there is no further correlation between the experiments.…”

Assessment of hand kinematics and interactions with the environment

“…3.3) could not be explained by relative marker movement alone, but might have been caused by the segment calibration. A third study used a minimal set of markers (only on hand and distal phalanges of thumb and index) in combination with a mathematical model, to compare MCP, PIP and DIP angles with data obtained from a complete set of markers on all the individual segments [129]. The mean differences they found in joint angles between the two methods (8-17 deg) exceeded the differences we found between the PowerGlove and the OE system.…”

“…piezoresistive bend sensors or optical fiber sensors) [42]. The disadvantages of most of these systems are limited accuracy, need for complex calibration, line of sight problems (OE markers), crosstalk due to misalignment of sensors, poor robustness, or limited usability during functional tasks or in clinical practice [97,129,174].…”