# Automated Detection of Runs and Turns in Alpine Skiing Using GNSS Sensors

Jonas Arondel, Samuel Lacroix, Julien Clément

► To cite this version:

Jonas Arondel, Samuel Lacroix, Julien Clément. Automated Detection of Runs and Turns in Alpine Skiing Using GNSS Sensors. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the., 10.46298/mbj.16166. hal-05191718v2

HAL Id: hal-05191718

https://hal.science/hal-05191718v2

Submitted on 23 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Automated Detection of Runs and Turns in Alpine Skiing Using GNSS Sensors

Jonas Arondel<sup>a</sup>, Samuel Lacroix<sup>b</sup>, Julien Clément<sup>a, c*</sup>

<sup>a</sup> École de technologie supérieure, Montréal, Canada

<sup>b</sup> Ski Québec Alpin, Montréal, Canada

<sup>c</sup> Institut National du Sport du Québec, Montréal, Canada

* Corresponding author: julien.clement@etsmtl.ca

Received date: 03/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** alpine skiing, external training load, wearable sensor

© 2025 The Authors
Licence CC-BY 4.0
Published by Société de Biomécanique

## 1. Introduction

The rise of wearable sensors has revolutionized sports analysis by enabling an objective quantification of athletes' efforts. In alpine skiing, GPS and inertial measurement units (IMUs) are the most commonly used sensors, facilitating applications such as performance assessment (Supej and Holmberg, 2011), determination of external forces (Gilgien, et al., 2013), or turn detection (Benedikt et al., 2018). These sensors continuously provide data on skiers' position, speed, and acceleration. However, current tools for processing and analyzing these data remain largely manual, requiring coaches to define start and finish lines of runs, count turns, and track motor engagement duration, leading to increased workload and a higher risk of errors.

This study aims to develop an automated method to detect turns and runs, including start and finish times, enhancing data processing efficiency by eliminating manual operation.

## 2. Methods

### 2.1 Data acquisition

Ethical approval was granted by ÉTS. Data were collected from six competitive Quebec Ski team athletes across Super-G, Giant Slalom, and Slalom disciplines. Training sessions took place in multiple locations across two countries, with a total of 1006 runs analyzed (Super-G: 98, Giant Slalom: 452, Slalom: 456). All runs, including abandoned runs, errors and stradles were considered.

Athletes were equipped with Protern.io Global Navigation Satellite System (GNSS) sensors (Protern, Whitehorse, YU, Canada, 10 Hz) (Jølstad, et al., 2021), positioned on their upper backs, to track spatial and kinematic data throughout each training session.

### 2.2 Data processing

GNSS sensors recorded athletes' latitude, longitude, and altitude. These data, initially expressed in the World Geodetic System 1984 (WGS84), were converted into planar coordinates using the appropriate Universal Transverse Mercator (UTM) zone projection.

### 2.3 Event detection

**Run detection:** Changes in altitude over time were analyzed to identify the number or runs.

**Turn detection:** The angle formed by three trajectory points (Supej and Nemec, 2003) was compared to a threshold to identify turns (166° for Slalom, 170° for Super-G and Giant Slalom).

**Start of run detection:** Before the start, athletes remain stationary at the top of the run, resulting in a non-linear trajectory. The transition from the waiting phase to the start of the run was identified by calculating the angle between three trajectory points and comparing it to a predefined threshold. The trajectory was deemed stable when the angle remained above 120° for at least 10 seconds. The start was then defined as the first point the angle reached 170°.
**End of run detection:** The divergence point of athletes' trajectories after crossing the finish line was used, supposing it marks the run's end.

The thresholds were empirically adjusted for each discipline and were the same for all athletes.

## 2.4 Validation Protocol

To establish the ground truth for run counts, the altitude graph was manually analyzed. Start detection accuracy was evaluated by comparing automated results to manually annotated start points (Fig. 1). For turn detection, 60 randomly selected runs were analyzed, with turns counted manually to validate algorithmic performance.

## 2.5 Statistics Analysis

For start detection, the time taken by the athletes between the ground truth starting line and the predicted starting line was measured in seconds (Fig. 1). Since the distribution was not normal, median values were calculated for each discipline. All runs were processed, including slow runs, aborted attempts, errors, and falls which led to several abnormally long values.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Trajectories with the predicted starting line in blue and the ground truth in red.</figcaption></figure>

The difference (% of error) between the number of turns detected by the algorithm and the ground truth was calculated for the selected 60 runs. The slaloms had 10 runs of 47 and 48 turns, GS 17 and 27, and SG 17 and 12. Results were averaged for each discipline.

## 3. Results and discussion

The automated run detection algorithm achieved 100% accuracy, correctly identifying all runs. The accuracy of start and turn detections are given by the Table 1.

<table><caption>**Table 1.** Accuracy of Start Time and % of Error for Number of Turns per Run Across Disciplines.</caption><thead><tr><th>Event</th><th>Discipline</th><th>Median</th><th>Mean</th></tr></thead><tbody><tr><td>Start</td><td>GS</td><td>0.28s</td><td></td></tr><tr><td>Start</td><td>SG</td><td>0.63s</td><td></td></tr><tr><td>Start</td><td>SL</td><td>0.39s</td><td></td></tr><tr><td>ΔTurns/run</td><td>GS</td><td></td><td>6%</td></tr><tr><td>ΔTurns/run</td><td>SG</td><td></td><td>4%</td></tr><tr><td>ΔTurns/run</td><td>SL</td><td></td><td>2%</td></tr></tbody></table>

Since start detection serves as an aid rather than an absolute reference, error under 1 s remain within an acceptable range for coaching purposes. Similarly, a discrepancy of 6%, corresponding to less than 2 turns, in Giant Slalom does not hinder performance monitoring, as the focus is on identifying trends and workload distribution rather than determining exact turn counts.

A key limitation of this study lies in the validation methodology. The ground truth for run and turn detection was based on manual annotations, which, while effective, may introduce subjectivity. A more precise validation approach would have involved video recordings synchronized with GNSS data, allowing for direct verification of key events such as start times, turns, and finish crossings, but this would remain difficult to apply for super-G events.

## 4. Conclusion

This study presents an automated approach to detecting runs and turns in alpine skiing using GNSS sensors, significantly reducing the need for time-consuming manual annotation. The developed algorithm demonstrated accuracy, detecting 100% of runs and providing a reliable estimate of the number of turns and starting line.

Integrating this tool into skier training could enhance external load monitoring and optimize performance analysis. This study marks a step toward more precise and automated performance quantification in alpine skiing, paving the way for more advanced analytical tools for athletes and coaches.
## Acknowledgements

The authors would like to thank Quebec Ski team for the data collection.

## Conflict of Interest Statement

None.

## Contributor Roles

JA: Methodology, Data analysis, Writing original draft; SL: Data curation, Supervision; JC: Methodology, Funding acquisition, Validation, Writing-review and editing.

## Funding

This work was funded by the synergistic Ski Alpin Québec funds, and by the Mitacs Acceleration Funds (Canada).

## References

Federolf, P. (2012). Quantifying instantaneous performance in alpine ski racing. *Journal of Sports Sciences, 30*, 1063–1068. https://doi.org/10.1080/02640414.2012.690073

Benedikt, F., Matthias, G., Gerber, N., Falbriard, M., Müller, E., Aminian, K., & Spörri, J. (2018). IMU and GNSS-based turn switch detection in alpine ski racing. *Science and Skiing VII*, 86–92.

Gilgien, M., Spörri, J., Chardonnens, J., Kröll, J., & Müller, E. (2013). Determination of external forces in alpine skiing using a differential global navigation satellite system. *Sensors*, 13(8), 9821–9835. https://doi.org/10.3390/s130809821

Supej, M., & Nemec, B. (2003). Kinematic determination of the beginning of a ski turn. *Kinesiologia Slovenica*, 9(1). https://www.dlib.si/details/URN:NBN:SI:DOC-4QSOYHV9

Supej, M., & Holmberg, H.-C. (2011). A new time measurement method using a high-end global navigation satellite system to analyze alpine skiing. *Research Quarterly for Exercise and Sport*, 82, 400–411. https://doi.org/10.1080/02701367.2011.10599772

Jølstad, P., Reid, R., Gjevestad, J., & Gilgien, M. (2021). Validity of the AdMos, advanced sport instruments, GNSS sensor for use in alpine skiing. *Remote Sensing*, 14, 22. https://doi.org/10.3390/rs14010022