To meet the set challenges, an iterative development approach is pursued, based on the creation of prototypes, which are continuously evaluated by the participants. This is useful to minimize the risk of building a system that does not meet the specified requirements. Furthermore, this project combines the development of machine learning algorithms with the creation and extension of climbing software.

Within the framework of the project, the various sensors and the data obtained from them will be examined and interpreted for their suitability for sports. The prototypes obtained in this way can be continuously refined and expanded during the course of the project and the data evaluation possibilities documented.

When selecting the sensors, the aim will be to keep the costs for the application of the developed sensor technologies low for the stakeholders. Solutions which require an expensive modification of the climbing grips (e.g. by using sensors in the grips of the climbing routes) will be excluded from the outset.

To test the developed prototypes, routes in climbing halls with different degrees of difficulty will be equipped with the tested sensors in order to carry out realistic experiments.

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