ArticleSensors (Basel, Switzerland)2021
From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- Artificial Intelligence is a Useful Tool in Exercise Science and Sports Medicine.Medicine and science in sports and exercise · 2026Article
- Fine reconstruction of badminton swing dynamic trajectory assisted by event camera.Scientific reports · 2026Article
- Intelligent strength training for football players using resnext optimized by upgraded chimp optimization algorithm.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
- Performance tracking in female youth soccer through wearables and subjective assessments.Frontiers in sports and active living · 2025Article
- Novel Cost-Effective and Portable Three-Dimensional Force Measurement System for Biomechanical Analysis: A Reliability and Validity Study.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Badminton Game Analysis: An Approach to Shot Refinement via a Fusion of Shuttlecock Tracking and Hit Detection from Monocular Camera.Sensors (Basel, Switzerland) · 2024Article
- IMU-Based Fitness Activity Recognition Using CNNs for Time Series Classification.Sensors (Basel, Switzerland) · 2024Article
- Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball?Sensors (Basel, Switzerland) · 2023Article
- Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players.Sensors (Basel, Switzerland) · 2023Article
- xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Wearable Sensors for Activity Recognition in Ultimate Frisbee Using Convolutional Neural Networks and Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Optimization of Ideological and Political Education Strategies in Colleges and Universities Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots.Frontiers in neurorobotics · 2022Article
- Automatic Detection Algorithm of Football Events in Videos.Computational intelligence and neuroscience · 2022Article
- A Survey of Human Gait-Based Artificial Intelligence Applications.Frontiers in robotics and AI · 2021Review
- Deep Learning-Based Teaching Strategies of Ideological and Political Courses Under the Background of Educational Psychology.Frontiers in psychology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
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.
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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.