Automatic Joint Parameter Estimation from Magnetic Motion CaptureData James F.O”Brien Robert E. Bodenheimer Gabriel J Brostow Jessica K. Hodgins Presented by Ws Hong.

Goal Limb linghts, joint locations And sensor placement Mocap data for Human subject determine A hierarchical stucture inferred Perform FK and IK procedures 1. An automatic method for computing limb lengths, joint locations and sensor placement From magnetic motion capture data 2. The result of using the algorithm on mocap data and validation result from simulation

The related work Inside of graphics 1. Silaghi and colleagure[18] Iidentifying an anatomic skeleton from opticla motion capture Data 2. Bodenheimer and colleagure[2] Inverse kinematics are often used to extract joint angles from gobal position data Outside of graphics 1. Biomechanics[15,16] The problem of determining a system’s kinematic parameters from the motion of the system 2. Robotics[15,16] Interested in similar questions because they need to calibrate physical devices

Methods Arrows : outboard direction Let Transformation …for Ith body coordinate to jth Body coordinate Translational component Rotational component

Transformation : I-th coordinate system to j-th coordinate system It may be inverted In terms of Ci and li….

Finding Joint Location By applying to both sides To matrix form

3n by 6 matrix 3n by 1 matrix After determination of the locations for the joints….. The Body Hierarchy : Each body  a node Joints  edge between body Joint fit error  Weight of edge Minimal spanning tree Determinate the Hierarchy

Result Test on something less complicated than bio-logical joints Wooden mechanical linkage with 5 ball Motion capture sensors The model computed from Mocap data

A comparison of measurements and calculated limb length for six data sets of the Mechanical linkage The maximum error is 1.1 The hierarchy was computed correctly The residual vectors from the least squares process All data is less than 1.0 The error is on the order of the resolution of the sensors

Comparing residual errors between the mechanical linkage and a male subject Residual errors of the right shoulder for the mechanical linkage Residual errors of the data from Walk2 of a male subject  Error is Much larger than for the mechanical linkage

A comparison of measurements and calculated limb lengths for four data sets of a male subject Maximum difference 4.1 at left upper arm Find big error in left upper arm continuesly  due to an error measured by hand? Maximum difference –2.4 is also at left upper arm It is less than that for male test Mean difference for more than 1centimeters  right lower leg, left upper leg, left upper arm

Conclusion 1.An automatic method for computing limb lengths, joint locations and sensor placement from magnetic motion capture data. 2.Produced results accurate to the resulution of the sensors for data. 3.The algorithm would also be of use in applications for the problem fitting data to a graphical model 4.The algorithm for marker identification can be used to extract the hierarchy automatically.