Improvement of the passing quality of an American football training machine

Hollaus, Bernhard; Raschner, Christian; Mehrle, Andreas

doi:10.1177/1754337120975225

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…This system should independently record and store an audio and video sequence of an athlete during a catch attempt via an external trigger. The recording system is integrated into the passing machine [ 29 , 30 ], which triggers the start of the recording. A camera and a microphone with a directional pattern are mounted on the ball-throwing machine.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The data that are necessary to train the networks were gathered in an experiment. The experimental setup always consisted of a passing machine [ 30 ] which also carried the recording system. The recording system was connected to rotate horizontally according to the azimuth of the launch unit of the passing machine.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…All the given systems have no open interface for integrating external hardware. Therefore the passing machine, which was designed by Hollaus et al [ 29 , 30 ], is the only one that enables the development of the catch recognition system, based on audio and video recordings. Still, the passing machine and the recording system only enable a profound analysis of the catching abilities of receiving athletes but an algorithm that analyzes is still missing.…”

Section: Introductionmentioning

confidence: 99%

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Catch Recognition in Automated American Football Training Using Machine Learning

Hollaus

Reiter

Volmer

2023

Sensors

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In order to train receivers in American football in a targeted and individual manner, the strengths and weaknesses of the athletes must be evaluated precisely. As human resources are limited, it is beneficial to do it in an automated way. Automated passing machines are already given, therefore the motivation is to design a computer-based system that records and automatically evaluates the athlete’s catch attempts. The most fundamental evaluation would be whether the athlete has caught the pass successfully or not. An experiment was carried out to gain data about catch attempts that potentially contain information about the outcome of such. The experiment used a fully automated passing machine which can release passes on command. After a pass was released, an audio and a video sequence of the specific catch attempt was recorded. For this purpose, an audio-visual recording system was developed which was integrated into the passing machine. This system is used to create an audio and video dataset in the amount of 2276 recorded catch attempts. A Convolutional Neural Network (CNN) is used for feature extraction with downstream Long Short-Term Memory (LSTM) to classify the video data. Classification of the audio data is performed using a one-dimensional CNN. With the chosen neural network architecture, an accuracy of 92.19% was achieved in detecting whether a pass had been caught or not. The feasibility for automatic classification of catch attempts during automated catch training is confirmed with this result.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Catch Recognition in Automated American Football Training Using Machine Learning

Hollaus

Reiter

Volmer

2023

Sensors

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“…Media dinding disini mengacu kepada sebuah penciptaan, penyesuaian dan menampilkan suatu alat/sarana dan prasarana yang baru, unik, dan menarik terhadap suatu proses belajar mengajar pendidikan jasmani. Permainan sepakbola sangat di gemari oleh masyarakat seluruh dunia, salah satunya di indonesia mulai dari kota sampai pelosok desa,dari sekolah dasar sampai perguruan tinggi, khusunya sekolah dasar, antusias para siswa sekolah dasar sangat penasaran tentang permainan sepakbola, tetapi masih banyak siswa yang belum paham mengenai tehnik dasar sepakbola khususnya passing (Burke, 2021;Håland, 2020;Hollaus, 2021;Mclean, 2018;Zhou, 2023), hal ini di buktikan banyaknya siswa yang berminat mengikuti pembelajaran sepakbola tetapi terbukti semua anak bisa menendang bola tetapi masih banyak siswa yang belum megerti keterampilan tehnik dasar sepakbola passing (mengoper bola) dengan baik dan benar.…”

Section: Pendahuluanunclassified

Peningkatan Passing Sepakbola Menggunakan Media Dinding pada Siswa Kelas V di MI Darul Ulum Sumberagung

Ali,

Apriliyanto,

Usman

et al. 2023

jpo

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This research employs Classroom Action Research (CAR). Data collection methods are essential for obtaining accurate and accountable information. The choice of research methods for data collection aligns with the research objectives, utilizing observation and measurement techniques. Data analysis techniques encompass both qualitative and quantitative approaches. Qualitative descriptive analysis aims to describe collaborative observation results and compare them with the number of observed students. Quantitative descriptive analysis, on the other hand, seeks to compare the observations made by the researcher and collaborators regarding student behavior during the learning process. This research was conducted in Class V of MI Darul Ulum Sumberagung and resulted in a successful outcome.

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Improvement of the passing quality of an American football training machine

Cited by 2 publications

References 14 publications

Catch Recognition in Automated American Football Training Using Machine Learning

Catch Recognition in Automated American Football Training Using Machine Learning

Peningkatan Passing Sepakbola Menggunakan Media Dinding pada Siswa Kelas V di MI Darul Ulum Sumberagung

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