3D CROSS-SECTIONAL SHAPE MEASUREMENT OF THE FOOT WHILE WALKING

Makoto Kimura1,2

, Masaaki Mochimaru1,2

, Makiko Kouchi1,2

, Hideo Saito3, Takeo Kanade

1,2,4

1 Digital Human Research Center, National Institute of Advanced Industrial Science and Technology

2 CREST, Japan Science and Technology Agency

3 Department of Information and Computer Science, Keio University

4 The Robotics Institute, Carnegie Mellon University

INTRODUCTION

There are many 3D body scanner systems. However, there is no system that can measure 3D human

body shape in motion. Using techniques of computer vision, we have developed a system that can

capture foot shape during walking. In this paper, we present the principle and algorithms of our

measurement system and the measured data of feature cross-sections of the foot during walking.

PRINCIPLE

Most of the existing 3D shape measurement systems use laser scanning[2], and requires a time (several

seconds, typically) for measurement. The principle of laser scanning measurement is triangulation

between camera and laser projector.

In the field of computer vision, 3D shape of the target object is reconstructed from multiple images

using triangulation between multiple cameras (stereo matching)[1]. Theoretically, this method can

measure an object as accurate as the laser scanning. Actually, it could not because of difficulties in pre-

process calibration and in finding accurate matching points in multiple images

PROPOSED SYSTEM

Our system uses eight sets of IEEE1394 camera and PC. All cameras see a common 3D area, which is

about 15cm cube. The arrangement of all cameras is heuristically optimized for our purpose. The

cameras are triggered with 70 msec intervals, so that they can capture completely synchronized

1024x768 color images. All cameras are calibrated by Tsai’s method[3] with additional improvement.

As a first goal, we intend to measure the 3-Dimensional transformation of three feature cross-sections

(ball, instep, and heel) during walking. The target cross-sections are painted with individual color (red,

yellow, and blue) before capturing. First, the painted regions are detected in each 2D image by using

image processing technique. Next, center line of each colored region is estimated. Finally, stereo

matching method is applied to each pixel on these center lines in the images. Our stereo matching

process uses some weak restriction, which assumes that the target is a cross-section of the foot.

Additionally, we process stereo matching in sub-pixel order to improve the accuracy. In our experiments,

the accuracy of camera calibration was less than 1mm. The output of our system includes some obvious

outliers, and the experimental maximum error of inliers was about 1mm.

EXPERIMENTS

We measured four subjects (two males and two females) in the walking motion. Three cross-sections

were painted on the right foot, and two landmarks were marked on each cross-section. Cross-sections at

different frame were superimposed using these marks. Four measurements were taken for each frame. In

our experiments, changes in the cross-section shape and dimensions were similar for all the subjects.

Figure 1 shows an example of experimental results. The breadths of feature cross-sections and arch

lengths change during the walking motion. Figure 2 shows the shape changes in ball, instep, and heel

cross-sections.

Patterns of the change of the medial arch length and ball breadth are similar to those measured by an

electric arch gauge[4]. Each cross-section seems to remain on a plane most of the stance phase. Shapes of

ball and heel cross-sections change due to the tendons appearing when the joint flexes.

Frame #12 Frame #15 Frame #18

DISCUSSIONS AND FUTURE WORK

We constructed a 3D measurement system for foot while walking. Because it still remains a little noise

and un-robustness - the error in detection of colored region and failure of finding matching points-, it

still needs more improvement. In the current system, we can capture only upper side of foot. In our plan,

the sole of foot will be acquired using glass floor. These are our future work.

REFERENCES

[1] D.H. Ballard, et al, (1982) “Computer Vision”, ISBN 0–13–165316–4, Prentice-Hall, INC.

[2] M. Kouchi and M. Mochimaru, (2001). “Development of a low cost foot-scanner for a custom shoe making system”, 5th

ISB Footwear Biomechanics, pp. 58–59., Zurich, Switzerland.

[3] R.Y. Tsai, (1987) “A versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using

Off-the-Shelf TV Cameras and Lenses”, IEEE Journal of Robotics and Automation, Vol. RA-3, No. 4, pp. 323–344.

[4] S.M. Yang et al. (1985) Dynamic changes of the arches of the foot during walking. Biomechanics IX-A:417-422.

Figure1: The breadth of feature cross-sections and length between cross-

sections change through walking.

Figure2: The shapes of feature cross-

sections projected onto the optimal

plane. Registration between frames is

done by landmark’s geometry.

Frame #12

Frame #15

Frame #18

MT

MH

Medial Lateral

MI