Modares Mechanical Engineering

Modares Mechanical Engineering

Assessing Postural Instability in Individuals with Low Back Pain Through Measurement of Center of Pressure and Sport Intensity: A Machine Learning Approach

Document Type : Original Article

Authors
Pourya Dorostkar Rouhani
Mahmood Azghani
Amin Partovifard
Mahrad Pouryosef Miandoab
Department of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
10.48311/mme.2026.119233.82975
Abstract
Falling is a major public health concern, especially for those dealing with low back pain, as it can lead to severe physical and psychological setbacks. While traditional clinical tests often miss the subtle nuances of balance impairment, quantitative Center of Pressure metrics offer a much clearer picture of stability. By combining these COP features with the right exercise intensity and advanced machine learning, we can create a more powerful tool for predicting and analyzing balance issues in patients with back pain.

In this study, we collected Center of Pressure data and extracted the key features defining postural stability. Following rigorous preprocessing and normalization, we evaluated several machine learning models specifically Logistic Regression, Random Forest, and Support Vector Machines to classify individuals with stable versus impaired balance.

The results demonstrated that these machine learning architectures performed exceptionally well in distinguishing between the two groups. Among the features extracted from the Center of Pressure data, velocity metrics and displacement amplitude emerged as the most potent predictors of poor balance. Through stratified cross-validation, our models achieved high accuracy and stability, providing an effective and reliable framework for identifying balance deficits
Keywords
Center of pressure
Physical Exertion
low back pain
Neural Networks
Computer
Postural Balance
Subjects
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Modares Mechanical Engineering
Volume 26, Issue 12
December 2026
Pages 949-958