Modeling and Predicting the Human Heart Rate During Running Exercise

Füller, Matthias; Sundaram, Ashok M.; Ludwig, Melanie; Asteroth, Alexander; Prassler, Erwin

doi:10.1007/978-3-319-27695-3_7

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Another underdeveloped research area concerns wearable devices that are used for sports rehabilitation (Mencarini et al, 2019 ). Even if the use of wearables in elite sports turns out to be unsuitable in some cases, due to e. g. competition rule systems, they can very well be used for training and during rehabilitation (Füller et al, 2015 ; Ludwig et al, 2015 , 2019 ; Schaefer et al, 2015 ). However, the research contains some gaps.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

Hammes¹,

Hagg²,

Asteroth³

et al. 2022

Front. Sports Act. Living

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This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

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