ArticleBMC sports science, medicine & rehabilitation2026
A study on the application of multimodal technologies in personalized training systems for the integration of physical education and general education.
Article in BMC sports science, medicine & rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundWith the deepening implementation of the “Physical Education and Teaching Integration” strategy, the role of physical education in the modern educational system has been increasingly emphasized. In the context of the “Double Reduction” policy, the transformation toward personalized, scientific, and information-driven teaching has become a key challenge.
methodsThis paper proposes a personalized instructional support system for physical education based on multimodal artificial intelligence technologies. The system is designed to deliver precise instructional interventions through action recognition, error analysis, and stratified feedback mechanisms.The system integrates video imagery and skeletal pose data, adopting Video MAE V2 as the core recognition framework. To enhance inference efficiency and real-time responsiveness, progressive knowledge distillation (PKD) and weighted inference with scaled ensemble (WISE) are introduced. Furthermore, a personalized feedback mechanism is developed that analyzes error vectors and generates stratified training recommendations tailored to individual students, thereby promoting differentiated instruction.
resultsThe proposed method achieves excellent performance on the UCF101 and HMDB51 datasets. Additionally, the model exhibits strong engineering applicability, maintaining efficient operation on edge computing platforms such as Raspberry Pi and Jetson Nano.
conclusionThe system holds promise for scalable applications in intelligent physical education.
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.