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Article

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

1
Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
2
Adidas AG, 91074 Herzogenaurach, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Anton Kos
Sensors 2021, 21(9), 3071; https://doi.org/10.3390/s21093071
Received: 26 February 2021 / Revised: 24 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities. View Full-Text
Keywords: data analysis; activity recognition; sensor-signal-based machine learning; deep learning; wearable sensors; sport data analysis; activity recognition; sensor-signal-based machine learning; deep learning; wearable sensors; sport
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MDPI and ACS Style

Stoeve, M.; Schuldhaus, D.; Gamp, A.; Zwick, C.; Eskofier, B.M. From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning. Sensors 2021, 21, 3071. https://doi.org/10.3390/s21093071

AMA Style

Stoeve M, Schuldhaus D, Gamp A, Zwick C, Eskofier BM. From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning. Sensors. 2021; 21(9):3071. https://doi.org/10.3390/s21093071

Chicago/Turabian Style

Stoeve, Maike, Dominik Schuldhaus, Axel Gamp, Constantin Zwick, and Bjoern M. Eskofier. 2021. "From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning" Sensors 21, no. 9: 3071. https://doi.org/10.3390/s21093071

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