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Applied Sciences
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Advances in Winter Sports and Data Science
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Advances in Winter Sports and Data Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1416

Special Issue Editors

Dr. Fumito Masui

E-Mail Website
Guest Editor
Kitami Institute of Technology, 165 Koencho, Kitami 090-0015, Hokkaido, Japan
Interests: winter sport; sport Informatics; curling informatics; tourism informatics; knowledge engineering; computation for figurative descriptions
Prof. Dr. Takashi Kawamura

E-Mail Website
Guest Editor
School of Science and Technology, Shinshu University, Nagano 390-8621, Japan
Interests: robotics and intelligent system; intelligent mechanics/mechanical systems
Prof. Dr. Yoshinari Takegawa

E-Mail Website
Guest Editor
Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Interests: UI/UX; AI; information desing; interaction; psychology of perception & cognition

Special Issue Information

Dear Colleagues,

We invite submissions to our Special Issue, which focusing on how data-driven technologies are transforming performance, strategy, and innovation in winter sports such as curling and alpine skiing.

Advances in data science, AI, and sensor technology are enabling new approaches to training, performance monitoring, tactical support, and equipment design. This Special Issue aims to showcase interdisciplinary research and practical developments at the intersection of computational science and winter athletics.

We welcome original research articles, technical reports, and applied studies addressing the following themes:

 - Theme 1: Performance and Strategy Optimization through Data Science

Contributions may include player/team analytics, predictive modeling, tactical simulations, or real-time data processing for winter sports.

 - Theme 2: Software-Based Tools for Motion Analysis and Skill Assessment

We seek studies on motion capture, video analysis systems, feedback-based training tools, and software for athlete evaluation.

 - Theme 3: Smart Systems and Innovative Sport Environments

Topics include IoT-based monitoring, AR/VR coaching tools, edge computing applications, and smart venue technologies.

 - Theme 4: New Technology in Winter Sports

We encourage submissions on 3D CAD in equipment design, embedded sensors, material optimization, and other engineering innovations for sports performance.

This Special Issue targets researchers and practitioners in sports science, data science, information engineering, and mechanical design. Studies should demonstrate clear contributions to the advancement of winter sports through data-oriented methods or systems.

Dr. Fumito Masui
Prof. Dr. Takashi Kawamura
Prof. Dr. Yoshinari Takegawa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • winter sports
  • curling
  • alpine skiing
  • data science
  • performance analytics
  • AI
  • sensor systems
  • 3D modeling
  • training support
  • smart systems

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Published Papers (2 papers)

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Research

19 pages, 1861 KB  
Article
Extraction of Stone Positions from a Sheet Image for Curling Match Database Construction
by Kei Suzumura, Yasumasa Tamura, Shimpei Aihara and Masahito Yamamoto
Appl. Sci. 2026, 16(7), 3453; https://doi.org/10.3390/app16073453 - 2 Apr 2026
Viewed by 388
Abstract
Curling is a sport in which two teams take turns delivering stones on ice and compete for total scores. It is a highly strategic sport, often referred to as “Chess on Ice”. In recent years, research on curling AI and statistical analysis aimed [...] Read more.
Curling is a sport in which two teams take turns delivering stones on ice and compete for total scores. It is a highly strategic sport, often referred to as “Chess on Ice”. In recent years, research on curling AI and statistical analysis aimed at tactical evaluation has been active. Decision-making in curling highly depends on the current stone position state, so obtaining stone positions is essential for tactical analysis. This study proposes an object detection model capable of acquiring stone coordinates with high accuracy and generality from stone position images of actual games. The proposed model was realized with a small amount of manually annotated data and pseudo-labeled images. Using the active testing method, the image-level accuracy of data—a strict criterion requiring perfect detection of all stones in a single image—for approximately 100,000 items was estimated to be 99.37%. Furthermore, we measured the positional error of the detected stones and found an average result of 0.472 px. We determined that this model had sufficient accuracy for practical use, so we decided to store the acquired coordinates in a database and use them as training data for the curling AI and statistical analysis. Full article
(This article belongs to the Special Issue Advances in Winter Sports and Data Science)
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17 pages, 840 KB  
Article
Attention-Enhanced LSTM for Real-Time Curling Stone Trajectory Prediction on Resource-Constrained Devices
by Guanyu Chen, Shimpei Aihara and Yoshinari Takegawa
Appl. Sci. 2026, 16(5), 2612; https://doi.org/10.3390/app16052612 - 9 Mar 2026
Viewed by 380
Abstract
Real-time trajectory forecasting for curling stones is essential for on-ice decision support, yet prior work often emphasizes offline analysis, fixed-window predictors, or physics-driven models that require additional measurements, and it rarely reports end-to-end feasibility under edge-computing constraints (latency and memory). This leaves a [...] Read more.
Real-time trajectory forecasting for curling stones is essential for on-ice decision support, yet prior work often emphasizes offline analysis, fixed-window predictors, or physics-driven models that require additional measurements, and it rarely reports end-to-end feasibility under edge-computing constraints (latency and memory). This leaves a practical gap between accurate trajectory reconstruction and deployable rink-side guidance. To bridge this gap, we propose an online forecaster based on low-dimensional (x,y) coordinate streams and a lightweight attention-enhanced Long Short-Term Memory (LSTM) architecture optimized for edge devices. The model uses a four-second sliding window (240 frames at 59.94 Hz) to predict fifteen seconds of future positions (900 frames) in a single multi-step forward pass, and an overlapping publication scheme is adopted to retain longer temporal context and stabilize continuous updates. We further provide a TensorFlow Lite (TFLite) conversion and quantization workflow to support on-device inference. Quantitatively, experiments on the CurlTracer dataset (1033 throws at 59.94 Hz) show that the proposed attention–LSTM achieves trajectory-level MAE/MdAE of 0.25/0.22 m over the full prediction horizon, improving over a plain LSTM (0.30/0.24 m) and a physics-based pivot-slide baseline (3.52/3.54 m). At two checkpoints, the first-step MAE/MdAE are 0.14/0.11 m and the mid-step MAE/MdAE are 0.21/0.18 m. For real-time feasibility, on a Raspberry Pi 4B the per-window latency is approximately 0.25 s (including I/O and post-processing), while CPU benchmarks show that TFLite variants provide 7–8× speedups over the original Keras runtime with only minor accuracy loss (e.g., window-level MAE 0.30–0.41 m across FP32/DRQ/FP16/INT8). Qualitatively, representative trajectory visualizations show good agreement in near/mid horizons and reasonable stopping-region guidance, supporting integration with a stone-mounted interface for actionable feedback. Full article
(This article belongs to the Special Issue Advances in Winter Sports and Data Science)
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