Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball

Markerless motion capture (MMC) offers a non-invasive method for monitoring external load in sports where wearable devices are restricted; however, its validity in 3x3 basketball contexts remains unverified. The viability and measurement precision of a multi-camera RTMPose-based MMC system for single-player tasks in 3x3 basketball performance monitoring were evaluated in this study. Recorded on a standard half-court, eight cameras (60 fps) captured ten collegiate athletes executing basketball-specific activities including linear sprints, curved runs, T-tests, and vertical jumps. The 3D coordinates of hip and ankle keypoints were reconstructed from multiple synchronized camera views using Direct Linear Transformation (DLT), from which horizontal displacement and average speed were derived. These values were validated using tape-measure distance and time-motion analysis. The MMC system demonstrated high accuracy, with coefficients of variation (CVs) below 5%, mean bias under 3.5%, and standard error of estimate (SEE) below 3% across most tasks. Speed estimates revealed great consistency with time-motion analysis (ICC = 0.97-1.00; standardized change in mean [SCM] varied from trivial to small). The Bland-Altman graphs verified no proportional error and little bias. These results confirm the MMC system as a consistent, non-invasive method for gathering movement data in outdoor basketball environments. Future studies should assess the system's performance during live competitive play with several athletes and occlusions and compare it to a laboratory-grade motion capture system.

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Publication Details

Journal
Sensors
Published
2025-06-27
DOI
https://doi.org/10.3390/s25134003
Citations
4
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
6.89

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Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball

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4 citations
Sensors
Sports Performance and Training
6.89
article

Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball

Mingxin Zhang, Rui Dong, Wei Wang, Wen Zheng, Mingjia Qiu
article en
4 citations

Abstract

Markerless motion capture (MMC) offers a non-invasive method for monitoring external load in sports where wearable devices are restricted; however, its validity in 3x3 basketball contexts remains unverified. The viability and measurement precision of a multi-camera RTMPose-based MMC system for single-player tasks in 3x3 basketball performance monitoring were evaluated in this study. Recorded on a standard half-court, eight cameras (60 fps) captured ten collegiate athletes executing basketball-specific activities including linear sprints, curved runs, T-tests, and vertical jumps. The 3D coordinates of hip and ankle keypoints were reconstructed from multiple synchronized camera views using Direct Linear Transformation (DLT), from which horizontal displacement and average speed were derived. These values were validated using tape-measure distance and time-motion analysis. The MMC system demonstrated high accuracy, with coefficients of variation (CVs) below 5%, mean bias under 3.5%, and standard error of estimate (SEE) below 3% across most tasks. Speed estimates revealed great consistency with time-motion analysis (ICC = 0.97-1.00; standardized change in mean [SCM] varied from trivial to small). The Bland-Altman graphs verified no proportional error and little bias. These results confirm the MMC system as a consistent, non-invasive method for gathering movement data in outdoor basketball environments. Future studies should assess the system's performance during live competitive play with several athletes and occlusions and compare it to a laboratory-grade motion capture system.

SensorsVol. 25(13)
South China Normal University (CN), Shanghai University of Sport (CN), Beijing Academy of Artificial Intelligence (CN), General Administration of Sport of China (CN), Shanghai Artificial Intelligence Laboratory (CN)
Shanghai Municipal Education Commission
Openalex Percentile: Top 3%
Sports Performance and Training
6.89
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