Evidence map›Paper›PMID 42076649›Full record

ArticleSensors (Basel, Switzerland)2026

Validity of a Commercially Available Inertial Measurement Unit for Artificial Intelligence-Based Trick Detection and Kinematic Performance Assessment in Skateboarding.

Birte Scholz, Niklas Noth, Maren Witt, Olaf Ueberschär

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Birte ScholzInstitute for General Training and Movement Sciences, Faculty of Sports Science, Leipzig University, 04109 Leipzig, Germany.
Niklas NothInstitute for Applied Training Science, 04109 Leipzig, Germany.ORCID 0009-0009-1340-258X
Maren WittInstitute for General Training and Movement Sciences, Faculty of Sports Science, Leipzig University, 04109 Leipzig, Germany.ORCID 0000-0003-2159-7785
Olaf UeberschärDepartment of Engineering and Industrial Design, Magdeburg-Stendal University of Applied Sciences, 39110 Magdeburg, Germany.ORCID 0000-0002-6822-5201

Funding

Leipzig University Open Access Fund
6 · The paper itself

Abstract

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE),

Indexed as

Artificial IntelligenceSkatingAdultBiomechanical PhenomenaFemaleHumansMaleMotion Captureartificial intelligencefreestyle sportskinematic analysismotion captureperformanceskateboardingtechnologytraining monitoringvalidation study

Identifiers

PMID42076649
PMCPMC13119854

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.