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.
Therefore we present a novel, holistic, and sensor-based approach for qualitative analysis of asynchronous, non-recurrent human motion. Furthermore, we deploy it to automatically assess the difficulty level of boulder routes on basis of climbing movements. Within a comprehensive study encompassing 153 ascents of 18 climbers, we extract and examine features such as strength, endurance, and control and achieve a successful classification rate of difficulty levels of more than 98%.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ebert, A., Schmid, K., Marouane, C., Linnhoff-Popien, C. (2018). Automated Recognition and Difficulty Assessment of Boulder Routes. In: Ahmed, M., Begum, S., Fasquel, JB. (eds) Internet of Things (IoT) Technologies for HealthCare. HealthyIoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-319-76213-5_9
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DOI: https://doi.org/10.1007/978-3-319-76213-5_9
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