Forró Trainer: automated feedback for partner dance learning

Augusto Dias Pereira Dos Santos, Kalina Yacef, Roberto Martinez-Maldonado

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

6 Citations (Scopus)

Abstract

In partner dance classes, teachers typically manage several students at the same time. For that reason, the amount of feedback provided in class is quite limited and students do not have many resources to get other feedback. In this demo paper, we present Forró Trainer, a tool that allows students to practice dance exercises by themselves, receiving automatically generated feedback about their performance. The system runs on a smartphone app and focuses on a fundamental aspect of dancing: learning to follow the rhythm of the music. The app detects the student's movements, using the mobile's accelerometer, extracts aspects of the rhythm and provides feedback. We present a description of the tool, the mistakes it detects, the automated feedback and the benefits that it may provide for dance students.

Original languageEnglish
Title of host publicationUMAP'17 - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
EditorsFederica Cena, Michel Desmarais
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages103-104
Number of pages2
ISBN (Electronic)9781450350679
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017
Conference number: 25th
https://www.um.org/umap2017/index.html
https://dl.acm.org/doi/proceedings/10.1145/3079628 (Proceedings)

Conference

ConferenceInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2017
Abbreviated titleUMAP 2017
Country/TerritorySlovakia
CityBratislava
Period9/07/1712/07/17
Internet address

Keywords

  • Automated feedback
  • Dance education
  • Motor learning
  • Wearable devices

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