Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings

Research Article

Automated Recognition and Difficulty Assessment of Boulder Routes

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  • @INPROCEEDINGS{10.1007/978-3-319-76213-5_9,
        author={Andr\^{e} Ebert and Kyrill Schmid and Chadly Marouane and Claudia Linnhoff-Popien},
        title={Automated Recognition and Difficulty Assessment of Boulder Routes},
        proceedings={Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings},
        proceedings_a={HEALTHYIOT},
        year={2018},
        month={2},
        keywords={Machine learning Activity recognition and assessment Climbing and bouldering},
        doi={10.1007/978-3-319-76213-5_9}
    }
    
  • André Ebert
    Kyrill Schmid
    Chadly Marouane
    Claudia Linnhoff-Popien
    Year: 2018
    Automated Recognition and Difficulty Assessment of Boulder Routes
    HEALTHYIOT
    Springer
    DOI: 10.1007/978-3-319-76213-5_9
André Ebert1,*, Kyrill Schmid1,*, Chadly Marouane1,*, Claudia Linnhoff-Popien1,*
  • 1: Ludwig-Maximilians-University
*Contact email: andre.ebert@ifi.lmu.de, kyrill.schmid@ifi.lmu.de, chadly.marouane@ifi.lmu.de, linnhoff@ifi.lmu.de

Abstract

Due to fast distribution of powerful, portable processing devices and wearables, the development of learning-based IoT-applications for athletic or medical usage is accelerated. But besides the offering of quantitative features, such as counting repetitions or distances, there are only a few systems which provide qualitative services, e.g., detecting malpositions to avoid injuries or to optimize training success.