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MRI-Based Skeletal Hand Movement Model

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The Human Hand as an Inspiration for Robot Hand Development

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 95))

Abstract

The kinematics of the human hand is optimal with respect to force distribution during pinch as well as power grasp, reducing the tissue strain when exerting forces through opposing fingers and optimising contact faces. Quantifying this optimality is of key importance when constructing biomimetic robotic hands, but understanding the exact human finger motion is also an important asset in, e.g. tracking finger movement during manipulation. The goal of the method presented here is to determine the precise orientations and positions of the axes of rotation of the finger joints by using suitable magnetic resonance imaging (MRI) images of a hand in various postures. The bones are segmented from the images, and their poses are estimated with respect to a reference posture. The axis orientations and positions are fitted numerically to match the measured bone motions. Eight joint types with varying degrees of freedom are investigated for each joint, and the joint type is selected by setting a limit on the rotational and translational mean discrepancy. The method results in hand models with differing accuracy and complexity, of which three examples, ranging from 22 to 33 DoF, are presented. The ranges of motion of the joints show some consensus and some disagreement with data from literature. One of the models is published as an implementation for the free OpenSim simulation environment. The mean discrepancies from a hand model built from MRI data are compared against a hand model built from optical motion capture data.

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Notes

  1. 1.

    voxel “volume pixel” = basic volume element of a 3-D image; analogous to pixel in 2-D images.

Abbreviations

MC:

Metacarpal bone

PP:

Proximal phalanx

PM:

Medial phalanx

PD:

Distal phalanx

CMC:

Carpometacarpal joint

IMC:

Intermetacarpal joint

MCP:

Metacarpophalangeal joint

PIP:

Proximal interphalangeal joint

DIP:

Distal interphalangeal joint

IP1:

Thumb interphalangeal joint

DoF:

Degree(s) of freedom

LOOCV:

Leave-one-out cross-validation

MRI:

Magnetic resonance imaging

MoCap:

(Optical) motion capture

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Acknowledgments

The authors would like to thank Karolina Stonawska for the tedious work of segmenting the bones. This project was partly funded by the EU project The Hand Embodied (FP7-ICT-248587).

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Authors and Affiliations

  1. Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany

    Georg Stillfried & Ulrich Hillenbrand

  2. Klinikum rechts der Isar, University hospital of TU München, Munich, Germany

    Marcus Settles

  3. Faculty of Informatics, Technische Universität München, Munich, Germany

    Patrick van der Smagt

Authors
  1. Georg Stillfried
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  2. Ulrich Hillenbrand
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  4. Patrick van der Smagt
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Corresponding author

Correspondence to Georg Stillfried .

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Editors and Affiliations

  1. Oregon State University, Corvallis, Oregon, USA

    Ravi Balasubramanian

  2. Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, USA

    Veronica J. Santos

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Stillfried, G., Hillenbrand, U., Settles, M., van der Smagt, P. (2014). MRI-Based Skeletal Hand Movement Model. In: Balasubramanian, R., Santos, V. (eds) The Human Hand as an Inspiration for Robot Hand Development. Springer Tracts in Advanced Robotics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-03017-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-03017-3_3

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