CONSTRUCTION OF AN ENERGY-METABOLISM MODEL FOR RUNNING AND OPTIMIZATION OF RUNNING PACE USING A GENETIC ALGORITHM

• Published in: Baiomekanizumu Vol. 16; pp. 231 - 241
• Main Authors: KOJIMA, Teruaki, TAKAMOTO, Shun’ichi, MORIOKA, Kenji, YAMAMOTO, Shinpei, WATANUKI, Masaya, HASEGAWA, Mitsuhiko, MIYAKE, Hitoshi, SHIONOYA, Akira
• Format: Journal Article
• Language: English; Japanese
• Published: Tokyo Society of Biomechanisms 2002; Society of Biomechanisms Japan
• ISSN: 1348-7116; 1349-497X
• DOI: 10.3951/biomechanisms.16.231

It is effective to determine running pace in advance, based on individual ability, in order to demonstrate the highest performance in long-distance running. The evaluation indices for a long-distance runner are maximum oxygen uptake, lactate threshold (LT), and ventilatory threshold (VT). These, however, are mostly used stastistically, so results may differ from real ability in a personal equation. The purposes of this study were to construct an energy-metabolism model and to optimize the running pace of long-distance running using a genetic algorithm (GA). The energy-metabolism model constructed in the study was composed of an anaerobic energy feeder structure, an aerobic energy feeder structure, and the section to be run. These elements were expressed as differential equations and restricted inequality formulas. The running speed for each subject, calculated from the best time for 300 meters, the amount of oxygen uptake, and running speed at the VT in each subject were used as parameters for the energy-metabolism model. VT was measured by a gradually increasing speed exercise using a treadmill because it was difficult to measure during field running. There are many differences between treadmill running and field running, however. In this study, the subject ran continuously on a treadmill with traction to his back using a rubber tube. The running speed for treadmill running was adjusted to that in field running based on heart rate. The energy-metabolism model had two controlled variables, and running speed could be controlled by these variables. We tried to optimize the energy-metabolism model by determining the two controlled variables using a GA. The spurt start point was also determined during optimization. The GA determined the spurt start point based on the energy-metabolism model. The running speed in 5000-meter races was optimized as follows: (1) speed ascends immediately after the start of the race, and then descends by a constant degree; (2) speed ascends again at 1000 to 1400 meters before the goal; and (3) almost 1 minute later, running goes to maximum speed then descends again by a constant degree all the way to the goal. This optimization result corresponded closely to the actual racing of the subject, who trained for improved ability in long-distance running.