Automatic Measurement Of Running Distance During Badminton Match Using Computer-based Trace Analysis

Introduction
It is necessary to know the workload of a match for designing training program in sports. In continuous exercise,
physical workload can be defined as distance of exercise from the starting point to the goal. However, running distance
depends on the match and should be measured in ball games. While historically a difficult variables to measure,
advancement in image processing technology make it relatively easy to measure running distance in some sports (1).
Image processor can find targets which has different color from the background in a picture. Players in a tennis courts
or badminton courts are such cases. So, we tried to measure running distance during badminton match in this study.

Methods
World high level men’s badminton singles match was video taped. The players attired in orange or black. The
badminton court was colored green. The location of the player in the computer screen was identified every 0.1 sec using
image processing computer G260 (DKH, Japan). The intersections of the court lines were used as calibration points to
calculate the location in the badminton court. Running distance, speed was calculated.

Results
Average and maximum moving speed during rally, total running distance were calculated from the location data. To
reduce the effect of noise 7 point moving average was applied to the location data. Table 1 shows the summary of thecalculation.

Discussion
In this study we measured running distance, speed in men’s singles badminton match with image processor. The
estimated average speed was 5.4 to 6.3 Km/h. This speed is about 1/4 of top marathon runners. So on the average
badminton exercise was aerobic exercise. Maximum speed was more than 20 Km/h, which is faster than most marathon
race. Besides, jumping and swinging racket is necessary in the badminton match, the exercise was considered anaerobic
in some situations. The running distance can be calculated from the average speed and duration. These data are useful to
design a training program.

References
[1]. Suda K et al. (2003) Applied Sports Science for High Performance in tennis, 151.

NOTA: O texto com a iconografia está no anexo

Arquivo