DOI: 10.5281/zenodo.17668359
title: Improving the quality of university sports assessment by integrating training behavior modeling with posture sequences and spatiotemporal attention mechanisms

description: Overview

This repository provides a reference implementation of the Dynamic Posture Behavior Integration Network (DPBIN) for university sports assessment. The framework jointly models training behavior and posture sequences and enhances evaluation through spatiotemporal attention mechanisms. It draws inspiration from recent research on integrating posture sequences, behavior features, and attention-based temporal reasoning to improve sports performance assessment in higher education.

The goal is to offer a compact yet extensible codebase that supports research, teaching, and prototype development for intelligent sports assessment systems.

Key Ideas

The Dynamic Posture Behavior Integration Network is built on three core components:

Posture Sequence Encoding

Pose keypoints are extracted frame by frame and converted into feature sequences representing joint coordinates, velocities, and basic kinematic information.

Training Behavior Modeling

Clip-level behavior statistics—such as speed, acceleration variance, rhythm consistency, and repetition counts—are incorporated as auxiliary features.

Spatiotemporal Attention Integration

A recurrent backbone processes posture features over time, while an attention module highlights movement phases and behavior patterns that are most informative for quality evaluation.

Together, these components generate a continuous quality score and, optionally, a discrete quality grade for each sports clip.

Datasets

The original study employs two university sports datasets that contain both video and posture information.




Dataset
Samples
Classes
Avg. Length (s)
Annotations




UATB (University Athlete Training Behavior)
7,200
6
6.0
Activity label, quality score, expert remarks


SPSD (Sports Posture Sequence Dataset)
9,600
3
5.5
Pose sequence, quality level




 

Method Summary

The implemented model follows the conceptual workflow described in the original research:

Posture Encoder

A lightweight projection network converts per-frame pose data into latent posture embeddings.

Behavior Encoder

A linear transformation maps behavior statistics into a behavior embedding.

Temporal Backbone

A bidirectional LSTM models temporal dynamics and captures contextual information across frames.

Spatiotemporal Attention

Attention weights are computed using both posture and behavior embeddings, producing context vectors that emphasize important frames and behavior attributes.

Fusion and Prediction

Temporal context and behavior embeddings are fused into a clip-level representation.A regression head predicts a continuous quality score, and an optional classification head outputs discrete quality labels.

Repository Layout




File/Folder
Description




model.py
Implementation of DPBIN with recurrent backbone and attention fusion


dataset.py
CSV-based dataset loader and tensor construction logic


train.py
Training driver script


inference.py
Prediction script for new samples


utils.py
Metrics, logging, and checkpoint utilities




Example Metrics

The following table summarizes representative results reported in the study:




Method
Acc (%)
Prec (%)
Rec (%)
F1 (%)
AUC
MAE
RMSE
Dataset




ResNet-50
91.4
90.7
89.5
90.1
95.1
0.73
0.89
UATB


TimeSformer
92.6
91.8
91.3
91.5
96.0
0.70
0.86
UATB


ST-GCN
93.1
92.0
91.8
91.9
96.5
0.68
0.84
SPSD


Two-Stream AGCN
93.5
92.4
92.2
92.3
96.7
0.66
0.82
SPSD


DPBIN (proposed)
94.9
93.8
93.6
93.7
97.9
0.59
0.74
Both




These metrics serve as reference benchmarks. Full reproduction requires access to the original datasets and preprocessing steps.

Extensibility

Potential extensions include:



Graph-based encoders for pose sequences

Joint regression–classification multi-task learning

Calibration-aware training for improved confidence estimation

Integrating expert annotations into the attention computation

Deploying the model for real-time assessment in online environments


License

This project is available under the MIT License. 

Citation

If this repository contributes to your research or teaching, please cite the original study on training behavior modeling, posture sequence analysis, and spatiotemporal attention for university sports assessment.