Time-based Simplified Denavit-Heartenberg Translation (TS-DH) for Capturing Finger Kinematic Data

Virtual finger model is commonly used in many applications for stroke fine motor rehabilitation especially in Virtual Reality (VR) applications. Capturing movement data for fingers is one of the important phases in any virtual fine motor rehabilitation process. Manual observation provides inconsistent evaluation given by different therapists for different rehabilitation sessions. Although the process of capturing data is performed, without time series of captured data, the data will not have a significant impact in producing consistent and meaningful evaluation. Furthermore, the consistency of the assessment of rehabilitation sessions will deliver more robust rehabilitation progress analysis. This data is very useful when paired with time information which can be analyzed to produce optimal evaluation. This paper proposes Time-based Simplified Denavit-Heartenberg Translation (TS-DH) consisting of forward kinematic with simplified DH parameter for capturing coordinate of end of each bone from virtual finger model paired with timeframe data. The DH model is enhanced by implementing 2 additional rules in assigning joint parameter. The data will be recorded with timeframe of every finger movement. As a conclusion, TS-DH model can be used in any virtual finger environment accurately.

HAND GRASP FUNCTION ANALYSIS BASED ON VF SET

This paper presents a method based on virtual finger (VF) set to analyze the grasp function of bionic hand. In order to exactly represent different function units of human hand or bionic hand a more detailed classification of VF is proposed to divide VF into three types: VFFE, VFAA and VFPalm, of which a VF set describing hand grasp can be composed. The VF set cannot only describe the degree of freedom (DOF) distribution of human hand or bionic hand but also indicate which fingers take part in grasp and what motion types the fingers are. The VF sets of eight classic bionic hands and five basic human hand grasp postures are provided. The inclusion relationship between the VF set of a bionic hand and those of the basic human hand grasp postures is used to analyze the grasp function of a bionic hand. As examples the grasp functions of several classic bionic hands, such as i-LIMB Hand, HIT/DLR Hand, etc. are analyzed.

Prehension Synergies: Trial-to-Trial Variability and Principle of Superposition During Static Prehension in Three Dimensions

We performed three-dimensional analysis of the conjoint changes of digit forces during prehension (prehension synergies) and tested applicability of the principle of superposition to three-dimensional tasks. Subjects performed 25 trials at statically holding a handle instrumented with six-component force/moment sensors under seven external torque conditions; –0.70, –0.47, –0.23, 0.00, 0.23, 0.47, and 0.70 Nm about a horizontal axis in the plane passing through the centers of all five digit force sensors (the grasp plane). The total weight of the system was always 10.24 N. The trial-to-trial variability of the forces produced by the thumb and the virtual finger (an imagined finger producing the same mechanical effects as all 4 finger forces and moments combined) increased in all three dimensions with the external torque magnitude. The sets of force and moment variables associated with the moment production about the vertical axis in the grasp plane and the axis orthogonal to the grasp plane consisted of two noncorrelated subsets each; one subset of variables was related to the control of grasping forces ( grasp control) and the other sassociated with the control of the orientation of the hand-held object ( torque control). The variables associated with the moment production about the horizontal axis in the grasp plane did not include the grip force (the normal thumb and virtual finger forces) and showed more complex noncorrelated subsets. We conclude that the principle of superposition is valid for the prehension in three dimensions. The observed high correlations among forces and moments associated with the control of object orientation could be explained by chain effects, the sequences of cause-effect relations necessitated by mechanical constraints.

Prehension Synergies in Three Dimensions

The goal of this study was to investigate the conjoint changes of digit forces/moments in 3 dimensions during static prehension under external torques acting on the object in one plane. The experimental paradigm was similar to holding a book vertically in the air where the center of mass of the book is located farther from the hand than the points of digit contacts. Three force and 3 moment components from each digit were recorded during static prehension of a customized handle. Subjects produced forces and moments in all 3 directions, although the external torques were exerted on the handheld object about only the Z-axis. The 3-dimensional response to a 2-dimensional task was explained by the cause–effect chain effects prompted by the noncollinearity of the normal forces of the thumb and the 4 fingers (represented by the “virtual finger”). Because the forces are not collinear (not along the same line), they generate moments of force about X- and Y-axes that are negated by the finger forces along the Y- and X-directions. The magnitudes of forces produced by lateral fingers (index and little) with longer moment arms were larger compared with the central fingers (middle and ring). At the virtual finger (an imaginary digit whose mechanical action is equivalent to the summed action of the 4 fingers) level, the relative contribution of different fractions of the resistive moment produced by subjects did not depend on the torque magnitude. We conclude that the CNS 1) solves a planar prehension task by producing forces and moments in all 3 directions, 2) uses mechanical advantage of fingers, and 3) shares the total torque among finger forces and moments in a particular way disregarding the torque magnitude.