A kinematic model of the human hand to evaluate its prehensile capabilities

Abstract

A kinematic model has been developed for simulation and prediction of the prehensile capabilities of the human hand. The kinematic skeleton of the hand is characterized by ideal joints and simple segments. Finger-joint angulation is characterized by yaw (abduction-adduction), pitch (flexion-extension) and roll (axial rotation) angles. The model is based on an algorithm that determines contact between two ellipsoids, which are used to approximate the geometry of the cutaneous surface of the hand segments. The model predicts the hand posture (joint angles) for power grasp of ellipsoidal objects by 'wrapping' the fingers around the object. Algorithms for two grip types are included: (1) a transverse volar grasp, which has the thumb abducted for added power; and (2) a diagonal volar grasp, which has the thumb adducted for an element of precision. Coefficients for estimating anthropometric parameters from hand length and breadth are incorporated in the model. Graphics procedures are included for visual display of the model. In an effort to validate the predictive capabilities of the model, joint angles were measured on six subjects grasping circular cylinders of various diameters and these measured joint angles were compared with angles predicted by the model. Sensitivity of the model to the various input parameters was also determined. On an average, the model predicted joint flexion angles that were 5.3% or 2.8 degrees +/- 12.2 degrees larger than the measured angles. Good agreement was found for the MCP and PIP joints, but results for DIP were more variable because of its dependence on the predictions for the proximal joints.

In vivo finger flexor tendon force while tapping on a keyswitch

“…The joint thickness and posture measurements taken from the video determined the subject-specific tendon moment arms as described by Armstrong and Chaffin (3) and An et al (1). Hand length measurements determine the distances between adjacent joint axes of rotation in the sagittal plane as described by Buchholz and Armstrong (5). To find a solution to the indeterminate set of equilibrium equations, we assumed muscle contraction balances the tension to minimize the sum of the squares of the muscle stress (7,20):…”

Hand Posture Prediction Using Neural Networks within a Biomechanical Model

“…Over the past two decades, a lot of research has been carried out in the field of robotic grasping [1][2][3][4] and it is currently a very hot topic. Several of the existing techniques can be extended to the field of biomechanics due to the similarities between human and robotic hands, although the former are kinematically simpler than the latter.…”

A Framework for Recognizing Grasps

“…Napier [26] regards volar grasps as power grasps and non-volar grasps as precision grasps. This view is shared by various other researchers in the medical and biomechanical fields (e.g., [28], [31]). However, this would not be strictly true if we adhere to the position that the type of grasp should be classified according to the predominance of either power or precision in the grasp (which, interestingly, Napier [26] adheres to as well).…”

“…Buchholz and Armstrong [28] model hand segments with ellipsoids for ease of analytic determination of contact points between the hand and held object, which is also modeled by an ellipsoid. The ellipsoid-ellipsoid con- …”