Evidence map›Paper›PMID 41257846›Full record

ArticleBMC sports science, medicine & rehabilitation2025

Footwork recognition and trajectory tracking of football goalie using Multi-Sensor technology.

Guoxi Ye, Lin Liang

Abstract read
In one paragraph

Article in BMC sports science, medicine & rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Guoxi YeDepartment of physical education, Shenzhen Polytechnic University, Shenzhen, 518055, Guangdong, China.
Lin LiangDepartment of physical education, Guangdong Medical University, Dongguan, 523808, Guangdong, China. lianglin2535@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In football penalty kicks, due to limited reaction speed and time requirements of the task, the goalkeeper must predict the direction of the ball’s flight before the opponent’s football touches. This article mainly focuses on the action recognition and tracking of penalty kick technology for football players on the football field. A system that integrates multiple sensors to solve the high requirements, privacy violations, and expensive equipment costs of scenarios and testing environments is proposed based on video image technology. The system collects 1,440 penalty action data from 24 subjects at the 11 m standard penalty spot using a 12 channel IMU embedded in the insole, and establishes a gait dataset. The trajectory analysis section extracts 30 segments from the competition video and manually marks 100 real motion trajectories for verification. The algorithm takes complementary filtering to fuse acceleration and angular velocity, combined with dual-mode convolutional neural network to achieve gait recognition, and completes trajectory tracking with Kalman filter as the core. The experimental results showed that the proposed algorithm achieved an area under the target tracking curve (AUC) of 91.15% in normal scenarios. In the case where members of the same team block the target, the maximum AUC was 67.73%, which was higher than that of comparative algorithms such as Multiple Instance Learning, Online AdaBoost, and Kernel Correlation Filter. This indicates that the proposed method is more accurate in target tracking than pure visual or single sensor schemes and has good robustness. In addition, the error between the estimated distance and the actual distance did not exceed 6%. The experimental results demonstrate that the proposed system breaks through the limitations of traditional video analysis in field and privacy, which can provide reliable data support for predicting penalty direction and optimizing actions in goalkeeper training.

Indexed as

FootballMotion data analysisMulti-sensor technologyPenalty techniqueTrajectory tracking

Identifiers

PMID41257846
PMCPMC12628954

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.