Observational studySensors (Basel, Switzerland)2019
Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers.
Observational study in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers' Perspective.JMIR mHealth and uHealth · 2024Pooled it
- Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.Biomimetics (Basel, Switzerland) · 2025Review
- The relationship between the 1200 m shuttle test and 40 m sprint test performance and distances covered in English Premier League matches: A retrospective two season study.Biology of sport · 2025Article
- Editorial: Assessment and monitoring of human movement.Frontiers in physiology · 2025Article
- In-field assessment of change-of-direction ability with a single wearable sensor.Scientific reports · 2023Article
- A review of machine learning applications in soccer with an emphasis on injury risk.Biology of sport · 2023Review
- Machine learning application in soccer: a systematic review.Biology of sport · 2023Review
- Effect of IMU location on estimation of vertical ground reaction force during jumping.Frontiers in bioengineering and biotechnology · 2023Article
- Predicting Coordination Variability of Selected Lower Extremity Couplings during a Cutting Movement: An Investigation of Deep Neural Networks with the LSTM Structure.Bioengineering (Basel, Switzerland) · 2022Article
- The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- Foot Position Measurement during Assistive Motion for Sit-to-Stand Using a Single Inertial Sensor and Shoe-Type Force Sensors.International journal of environmental research and public health · 2021Article
- Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units.Sensors (Basel, Switzerland) · 2021Article
- Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.Sensors (Basel, Switzerland) · 2020Article
- Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players.Sensors (Basel, Switzerland) · 2020Article
- Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data.Frontiers in bioengineering and biotechnology · 2020Article
- Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview.Applied bionics and biomechanics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the field. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artificial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classification returned good results (accuracy > 98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coefficient of determination R
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What OpenQuestion holds
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.