Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales

Conventional physical education assessment depends on subjective teacher observation and simple rating scales, often resulting in subjective bias, low evaluation efficiency, and delayed instructional feedback.

This study constructed a hierarchical multimodal fusion framework integrating inertial measurement unit (IMU) data and expert rubric scores for standardized PE skill evaluation. The framework adopts Dynamic Time Warping for spatiotemporal alignment to resolve asynchrony between continuous sensor signals and discrete manual scoring, and applies adaptive gated fusion to balance modality weights according to data quality. A total of 3,920 skill samples from 245 middle-school students across three schools and four sports were independently annotated by three calibrated PE teachers, achieving high inter-rater reliability (ICC(2,k) = 0.87, 95% CI [0.84, 0.90]). All experiments were conducted with participant-wise data splitting and ten repeated random-seed trials to ensure result stability.

The proposed multimodal model achieved an overall accuracy of 91.3 ± 0.4%, significantly surpassing single-modality baselines (p < 1 × 10⁻⁸). Spatiotemporal alignment effectively eliminated temporal mismatch, and the model maintained stable performance under simulated data missingness and sensor failure. The 43.2 ms single-sample inference speed meets real-time classroom assessment demands. Ablation experiments verified the essential role of cross-modal attention in multimodal learning. Leave-one-school-out and cross-stratum evaluations confirmed stable performance across different schools, genders, age groups and skill levels. The current multi-validation results support reliable within-population model performance, while external validation with independent cohorts is still required for broader generalizability. Model interpretability analysis further validated the biomechanical rationality of the learned assessment patterns.

This study provides a feasible multimodal digital assessment pipeline for standardized and formative physical education evaluation. The system intelligently reproduces expert-level teacher scoring rather than objective biomechanical measurement, which makes it a powerful auxiliary tool for classroom teaching rather than a substitute for professional teacher judgment. This work offers a practical digital transformation pathway for PE assessment, with standardized curriculum alignment and staged teacher professional development supporting reliable real-world educational deployment.