Evidence map›Paper›PMID 31766257›Full record

ArticleSensors (Basel, Switzerland)2019

Cross-Country Skiing Analysis and Ski Technique Detection by High-Precision Kinematic Global Navigation Satellite System.

Masaki Takeda, Naoto Miyamoto, Takaaki Endo, Olli Ohtonen, Stefan Lindinger, Vesa Linnamo, Thomas Stöggl

Open access · goldAbstract read
In one paragraph

Article 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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 13% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
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  9. Editorial: Health and Performance Assessment in Winter Sports.Frontiers in sports and active living · 2021
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 4 countries.

Masaki TakedaFaculty of Health and Sports Science, Doshisha University, Kyoto 610-0332, Japan.ORCID 0000-0002-2438-5692
Naoto MiyamotoNew Industry Creation Hatchery Center, Tohoku University, Sendai 980-8576, Japan.
Takaaki EndoFaculty of Health and Sports Science, Doshisha University, Kyoto 610-0332, Japan.
Olli OhtonenFaculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland.
Stefan LindingerDepartment of Food and Nutrition and Sport Science, University of Gothenburg, SE-405 Gothenburg, Sweden.ORCID 0000-0001-9346-1268
Vesa LinnamoFaculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland.
Thomas StögglDepartment of Sport and Exercise Science, University of Salzburg, 5020 Salzburg, Austria.ORCID 0000-0002-6685-1540
Doshisha University · JPUniversity of Jyväskylä · FIUniversity of Salzburg · ATTohoku University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cross-country skiing (XCS) embraces a broad variety of techniques applied like a gear system according to external conditions, slope topography, and skier-related factors. The continuous detection of applied skiing techniques and cycle characteristics by application of unobtrusive sensor technology can provide useful information to enhance the quality of training and competition. (1) Background: We evaluated the possibility of using a high-precision kinematic global navigation satellite system (GNSS) to detect cross-country skiing classical style technique. (2) Methods: A world-class male XC skier was analyzed during a classical style 5.3-km time trial recorded with a high-precision kinematic GNSS attached to the skier's head. A video camera was mounted on the lumbar region of the skier to detect the type and number of cycles of each technique used during the entire time trial. Based on the GNSS trajectory, distinct patterns of head displacement (up-down head motion) for each classical technique (e.g., diagonal stride (DIA), double poling (DP), kick double poling (KDP), herringbone (HB), and downhill) were defined. The applied skiing technique, skiing duration, skiing distance, skiing speed, and cycle time within a technique and the number of cycles were visually analyzed using both the GNSS signal and the video data by independent persons. Distinct patterns for each technique were counted by two methods: Head displacement with course inclination and without course inclination (net up-down head motion). (3) Results: Within the time trial, 49.6% (6 min, 46 s) was DP, 18.7% (2 min, 33 s) DIA, 6.1% (50 s) KDP, 3.3% (27 s) HB, and 22.3% (3 min, 03 s) downhill with respect to total skiing time (13 min, 09 s). The %Match for both methods 1 and 2 (net head motion) was high: 99.2% and 102.4%, respectively, for DP; 101.7% and 95.9%, respectively, for DIA; 89.4% and 100.0%, respectively, for KDP; 86.0% and 96.5%, respectively, in HB; and 98.6% and 99.6%, respectively, in total. (4) Conclusions: Based on the results of our study, it is suggested that a high-precision kinematic GNSS can be applied for precise detection of the type of technique, and the number of cycles used, duration, skiing speed, skiing distance, and cycle time for each technique, during a classical style XCS race.

Indexed as

Satellite CommunicationsAltitudeBiomechanical PhenomenaHumansImaging, Three-DimensionalMaleSkiingTime FactorsVideo Recordingclassical techniquecross-country skiingGPSkinematic GNSS

Identifiers

PMID31766257
PMCPMC6891545
OpenAlexW2985432479

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.