ArticleSensors (Basel, Switzerland)2022
Alpine Skiing Activity Recognition Using Smartphone's IMUs.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Motion Analysis in Alpine Skiing: Sensor Placement and Orientation-Invariant Sensing.Sensors (Basel, Switzerland) · 2025Article
- System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.Sensors (Basel, Switzerland) · 2024Article
- SnowMotion: A Wearable Sensor-Based Mobile Platform for Alpine Skiing Technique Assistance.Sensors (Basel, Switzerland) · 2024Article
- 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
- System Based on an Inertial Measurement Unit for Accurate Flight Time Determination in Vertical Jumps.Sensors (Basel, Switzerland) · 2023Article
- Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- Counting Activities Using Weakly Labeled Raw Acceleration Data: A Variable-Length Sequence Approach with Deep Learning to Maintain Event Duration Flexibility.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward's methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers' skill level in any condition. Among the studied methods and settings, the best model had 99.25% accuracy.
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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.