ArticleBMC sports science, medicine & rehabilitation2025
Classic action recognition of cross-country skiing sports combining ant colony and clustering algorithm.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Application of LSTM-CNN in skiing action recognition under artificial intelligence technology.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs one of the oldest sports events in world sports history, cross-country skiing involves basic techniques such as mountaineering, skiing, and gliding, covering multiple classic actions during the process.
methodsTo improve the current cross-country skiing training technology, this study designed a classic action recognition method for cross-country skiing that combines ant colony and clustering algorithms. This method mainly utilizes ant colony algorithm to classify and partition actions, and implements classical action recognition based on current data parameters. The research will improve the ant colony clustering algorithm compared to traditional K-means algorithm, hierarchical clustering algorithm, density clustering algorithm, and basic ant colony algorithm. Metrics such as the sum of squared intra-cluster errors are used to evaluate cluster tightness for validation analysis.
resultsThe results show that the proposed method achieves 92.3% accuracy in recognizing classical actions with a maximum improvement of 2.9% compared to the K-means algorithm. Meanwhile, the average percentage error value of this method is only 0.62. In the action recognition of athletes at different levels, the convergence decreased by 19%.
conclusionsFrom this, it can be concluded that the proposed method has high action recognition ability and recognition accuracy, which can effectively improve the recognition effect of cross-country skiing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.