Name
Abstract | ||
|---|---|---|
In sports, athletes need detailed and individualised training plans for maintaining and improving their skills in order to achieve their best performance in competitions. This presents a considerable workload for coaches, who besides setting objectives have to formulate extremely detailed training plans. Automated Planning, which has already been successfully deployed in many real-world applications such as space exploration, robotics, and manufacturing processes, embodies a useful mechanism that can be exploited for generating training plans for athletes.In this paper, we propose the use of Automated Planning techniques for generating individual training plans, which consist of exercises the athlete has to perform during training, given the athlete's current performance, period of time, and target performance that should be achieved. Our experimental analysis, which considers general training of kickboxers, shows that apart of considerable less planning time, training plans automatically generated by the proposed approach are more detailed and individualised than plans prepared manually by an expert coach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00655 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Field | DocType | ISSN |
Athletes,Engineering management,Workload,Computer science,Space exploration,Artificial intelligence,Machine learning,Robotics | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomás Skerík | 1 | 0 | 0.34 |
| Lukáš Chrpa | 2 | 20 | 6.19 |
| Wolfgang Faber | 3 | 1784 | 76.26 |
| Mauro Vallati | 4 | 216 | 46.63 |