AuthorsH. D. Johansen, D. Johansen, T. Kupka, M. Riegler, and P. Halvorsen
EditorsK. Truong, D. Heylen, M. Czerwinski, N. Berthouze, M. Chetouani, and M. Nakano
TitleScalable Infrastructure for Efficient Real-Time Sports Analytics
AfilliationCommunication Systems, Machine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2020
Conference NameCompanion Publication of the 2020 International Conference on Multimodal Interaction
PublisherACM
Place PublishedNew York, NY, USA
ISBN Number9781450380027
Keywordsalgorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging
Abstract

Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.

URLhttps://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi/10.1145/3395035.3425300https://dl.acm.org/doi/pdf/10.1145/3395035.3425300
DOI10.1145/339503510.1145/3395035.3425300
Citation Key27612

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