ArticleSensors (Basel, Switzerland)2020
Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed.
- Joint kinematic responses of Olympic medallist skiers to repeated slalom runs.Experimental physiology · 2025Article
- Unlocking Creative Movement with Inertial Technology.Brain sciences · 2025Article
- Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models.PloS one · 2025Article
- Distributed IMU Sensors for In-Field Dynamic Measurements on an Alpine Ski.Sensors (Basel, Switzerland) · 2024Article
- Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Skiing quality analysis of recreational skiers based on IMU data and self-assessment.Frontiers in sports and active living · 2024Article
- Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis.Frontiers in sports and active living · 2024Article
- Article
- SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports.Sensors (Basel, Switzerland) · 2022Article
- Alpine Skiing Activity Recognition Using Smartphone's IMUs.Sensors (Basel, Switzerland) · 2022Article
- Human Movement Quality Assessment Using Sensor Technologies in Recreational and Professional Sports: A Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones.Scientific reports · 2022Article
- Influence of Turn Cycle Structure on Performance of Elite Alpine Skiers Assessed through an IMU in Different Slalom Course Settings.Sensors (Basel, Switzerland) · 2022Article
- Effects of physical stress in alpine skiing on psychological, physiological, and biomechanical parameters: An individual approach.Frontiers in sports and active living · 2022Article
- Connected Skiing: Motion Quality Quantification in Alpine Skiing.Sensors (Basel, Switzerland) · 2021Article
- Observational and Accelerometer Analysis of Head Movement Patterns in Psychotherapeutic Dialogue.Sensors (Basel, Switzerland) · 2021Article
- Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance.Sensors (Basel, Switzerland) · 2021Article
- Comfortable and Convenient Turning Skill Assessment for Alpine Skiers Using IMU and Plantar Pressure Distribution Sensors.Sensors (Basel, Switzerland) · 2021Article
- Editorial: Health and Performance Assessment in Winter Sports.Frontiers in sports and active living · 2021Article
- Human and Animal Motion Tracking Using Inertial Sensors.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well.
Indexed as
Identifiers
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.