ArticleSensors (Basel, Switzerland)2022
An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Measurement Properties of Wearable Kinematic-Based Data Collection Systems to Evaluate Ball Kicking in Soccer: A Systematic Review with Evidence Gap Map.Sensors (Basel, Switzerland) · 2024Pooled it
- Deep learning for dynamic tactical formation recognition in professional football.Scientific reports · 2026Article
- The outcome prediction method of football matches by the quantum neural network based on deep learning.Scientific reports · 2025Article
- The optimization of youth football training using deep learning and artificial intelligence.Scientific reports · 2025Article
- A Diagnostic and Performance System for Soccer: Technical Design and Development.Sports (Basel, Switzerland) · 2025Article
- Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.Sensors (Basel, Switzerland) · 2024Article
- Sports activity (SA) recognition based on error correcting output codes (ECOC) and convolutional neural network (CNN).Heliyon · 2024Article
- Seamless Weft Knit Vest with Integrated Needle Sensing Zone for Monitoring Shoulder Movement: A First Methodological Study.Materials (Basel, Switzerland) · 2023Article
- Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.