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Updated: Sep 20, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation.

Nannan Xu¹, Xin Wang², Yangming Xu³

¹Sports Training Institute, Shenyang Sport University, Shenyang 110115, China.

Computational Intelligence and Neuroscience

|June 6, 2022

Summary

This study introduces a deep learning method for evaluating athlete balance control using pressure data. The novel model efficiently assesses balance, aiding athlete management and training.

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Area of Science:

Sports Science
Biomechanical Engineering
Artificial Intelligence

Background:

Athlete balance control is crucial for sports performance and injury prevention.
Current evaluation methods lack efficiency and intelligence, hindering athlete management.
Demand for automated and intelligent balance assessment is growing in sports training.

Purpose of the Study:

To propose a deep learning-based method for evaluating athlete balance control ability.
To develop an end-to-end model that processes raw time-series pressure data for direct evaluation.
To enhance practical utilization and efficiency in athlete balance assessment.

Main Methods:

Utilizing time-series movement pressure measurement data.
Implementing a multi-scale feature extraction scheme to capture diverse data patterns.
Employing a residual connected neural network architecture for efficient deep learning model training.

Main Results:

The proposed deep multi-scale residual connected neural network model demonstrated suitability for balance evaluation.
Experimental validation on real athlete tests confirmed the model's effectiveness.
The method showed promising results compared to existing approaches.

Conclusions:

The developed deep learning model offers an accurate and efficient solution for athlete balance control evaluation.
The end-to-end approach simplifies practical application in sports training and management.
This intelligent evaluation method holds significant potential for real-world sports scenarios.