Evidence map›Paper›PMID 40871807›Full record

ArticleSensors (Basel, Switzerland)2025

Real-Time Hand Tracking and Collision Detection for Immersive Mixed-Reality Boxing Training on Apple Vision Pro.

Alexey Karelin, Dmitry Brazhenko, Georgii Kliukovkin, Yehor Chernenko

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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.

Alexey KarelinIndependent Researcher, 30-392 Krakow, Poland.ORCID 0009-0001-0901-288X
Dmitry BrazhenkoIndependent Researcher, Seattle, WA 98107, USA.
Georgii KliukovkinIndependent Researcher, Sunnyvale, CA 94085, USA.ORCID 0009-0002-3323-1932
Yehor ChernenkoIndependent Researcher, San Francisco, CA 94103, USA.ORCID 0009-0008-3943-7636

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a real-time hand tracking and collision detection system for immersive mixed-reality boxing training on Apple Vision Pro (Apple Inc., Cupertino, CA, USA). Leveraging the device's advanced spatial computing capabilities, this research addresses the limitations of traditional fitness applications that lack precision for technique-based sports like boxing with visual-only hand tracking. The system is designed to provide objective feedback by recognizing boxing-specific gestures with sub-centimeter accuracy and validating biomechanical correctness during punch execution. A three-stage pipeline consisting of geometric filtering, biomechanical validation, and punch technique assessment rejects accidental or improper motions. Experimental evaluation involving 12 participants demonstrated a gesture recognition accuracy of 96.3% and a technique validation accuracy of 88.5%. The system consistently operated at 60 FPS with low latency and high robustness across diverse lighting conditions. These results indicate the potential of Apple Vision Pro as a platform for precision sports training and highlight the educational impact of mixed reality in democratizing access to high-quality boxing instruction. The proposed framework is extensible to other skill-based sports requiring fine motor control and real-time feedback.

Indexed as

HandAdultBiomechanical PhenomenaGesturesHumansMaleSportsApple Vision Proboxing trainingcollision detectiongesture recognitionhand trackingimmersive learningmixed realityspatial computingvirtual sports coaching

Identifiers

PMID40871807
PMCPMC12390506

What OpenQuestion holds

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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.