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Dr.-Ing. Ulf Jensen

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

The idea is really really simple, but what you get is almost like magic
Classification of Kinematic Golf Putt Data with Emphasis on Feature Selection
  • The complex movement sequences of golf require supporting tools for players and coaches alike. We developed a system that classifies the experience level and trained it with data from an inertial sensor on the club head. Based on 315 golf putts from eleven subjects the system differentiated between experienced and unexperienced players with a classification rate of 86.1%. To improve the classification system and obtain discriminant features we additionally integrated a feature selection step. We compared different selection approaches and concluded that a leave-subject-out feature selection was the appropriate approach to predict the true performance of a live system. The selected features can be fed back to coaches and help them to guide players to a better putting technique.

    Articles in Conference Proceedings
    Jensen, Ulf; Dassler, Frank; Eskofier, Björn
    Proceedings of the 21st International Conference on Pattern Recognition (21st International Conference on Pattern Recognition (ICPR 2012)), Tsukuba Science City, Japan, November 11-15, pp. 1735-1738, 2012 (BiBTeX, Who cited this?)