Dancing with horses: automated quality feedback for dressage riders

Robin Thompson, Ilias Kyriazakis, Amey Holden, Patrick Olivier, Thomas Plötz

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

13 Citations (Scopus)


The sport of dressage has become very popular not only amongst professional athletes but increasingly also for private horse owners. In well-defined tests, rider and horse execute movements, which demonstrate the strength, endurance, and dexterity of the animal as well as the quality of the interaction between rider and horse. Whilst at a professional level intensive expert coaching to refine the skill set of horse and rider is standard, such an approach to progression is not usually viable for the large amateur population. In this paper we present a framework for automated generation of quality feedback in dressage tests. Using on-body sensing and automated measurement of key performance attributes we are able to monitor the quality of horse movements in an objective way. We validated the developed framework in a large-scale deployment study and report on the practical usefulness of automatically generated quality feedback in amateur dressage.

Original languageEnglish
Title of host publicationUbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Subtitle of host publicationSeptember 7–11, 2015 Osaka, Japan
EditorsTanzeem Choudhury, Hans Gellersen, Koji Yatani
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Electronic)9781450335744
Publication statusPublished - 2015
Externally publishedYes
EventACM International Joint Conference on Pervasive and Ubiquitous Computing 2015 - Osaka, Japan
Duration: 7 Sep 201511 Sep 2015
Conference number: 3rd


ConferenceACM International Joint Conference on Pervasive and Ubiquitous Computing 2015
Abbreviated titleUbiComp 2015
Internet address


  • Activity recognition
  • Dressage
  • Horses
  • Skill assessment
  • Wearable sensing

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