DOI: 10.5281/zenodo.17369147
title: BASKETech

description: BASKETech: Basketball Technique Correction with Action Recognition and Temporal Feature Extraction

Overview

BASKETech is an intelligent system for basketball technique correction that integrates action recognition, temporal feature extraction, and an action comparison mechanism.This framework leverages advanced multimodal deep learning, combining spatial and temporal modeling, to provide real-time, personalized feedback on player technique.

The core architecture includes:



DTFEM (Dynamic Temporal Feature Extraction Model)

Multimodal Encoder Architecture

Adaptive Feedback Mechanism


Together, these modules enable high-precision recognition, real-time correction, and automated training insights, making BASKETech suitable for professional athletes, amateur players, and sports analytics research.



✨ Features

Dynamic Temporal Feature Extraction



Captures spatial features via convolutional layers and temporal dynamics using recurrent networks.

Integrates reduced self-attention, Mamba blocks, and context broadcasting networks.

Enables fine-grained recognition of dribbling, shooting, passing, and complex basketball movements.(See Fig. 1, p.7 for DTFEM architecture.)


Multimodal Encoder Architecture



Combines CNN and LSTM modules to model both frame-level details and long-term dependencies.(See Fig. 2, p.8 for encoder design.)

Supports dynamic local adapters for vision-language fusion, improving contextual understanding.


Action Comparison Mechanism



Aligns extracted player features with ideal reference techniques using similarity measures.

Computes similarity scores with Euclidean or cosine distance to highlight deviations.

Triggers adaptive corrective feedback for real-time improvement.


Adaptive Feedback Mechanism



Provides immediate and personalized training suggestions.

Enables player-specific performance monitoring and technique optimization over time.




📊 Datasets




Dataset
Description
Use




Basketball Player Motion Analysis Dataset
High-resolution annotated basketball motion sequences
Technique recognition and training


Temporal Feature Extraction in Sports Dataset
Time-sequenced video clips with event markers
Temporal modeling


Action Recognition in Basketball Dataset
Offensive & defensive plays annotated with labels
Model training and evaluation


Comparative Basketball Technique Dataset
Recordings of varied technique styles for comparison
Benchmarking and feedback optimization






 




🚀 Usage



Detected action labels

Temporal feature curves

Similarity scores vs. ideal technique

Real-time correction feedback




🧪 Applications



Real-time basketball technique correction

Performance monitoring and athlete development

Intelligent coaching and AR-assisted sports training

Sports biomechanics research and analytics




🧩 Model Components



DTFEM — Dynamic temporal feature extractor (Fig. 1, p.7).

Multimodal Encoder Architecture — CNN + LSTM integration (Fig. 2–4, p.8–10).

Action Recognition & Temporal Feature Strategy — vision-language transformer alignment (Fig. 3, p.9).

Adaptive Feedback Mechanism — similarity-based technique correction.

Graphical Propagation Layer — attention-based focus on key temporal segments.




📈 Performance




Dataset
Accuracy
Recall
F1 Score
AUC




Basketball Player Motion Analysis
89.67
89.12
88.45
88.89


Temporal Feature Extraction
91.12
90.56
89.89
90.34


Action Recognition in Basketball
89.67
89.12
88.45
88.89


Comparative Basketball Technique
91.12
90.56
89.89
90.23




This framework outperforms ResNet, ViT, I3D, BLIP, DenseNet, and MobileNet baselines.



🧭 Future Work



Optimize model for mobile and edge deployment.

Expand dataset to cover 3D skeletal pose estimation.

Enhance adaptability for player-specific technique templates.

Integrate with mixed reality or AR/VR training environments.




📜 License

This project is licensed under the MIT License.



🙏 Acknowledgments

This work was developed at School of Physical Education and Health, Zhaoqing University.Author: Zhixing Zhou.This project integrates deep learning and temporal analysis to support real-time, scalable basketball technique correction.