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Related Experiment Video

Updated: Jan 11, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

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InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain

Jeremy Carlosama1, Luis Zhinin-Vera2, Cesar Guevara3

  • 1School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary

Machine learning models accurately predict trunk mobility using inertial sensors, aiding personalized rehabilitation for low back pain (LBP). This technology offers objective clinical assessment and reduces healthcare costs.

Keywords:
ANOVAMachine learningclinical evaluationinertial sensorslow back painpredictive modelsregression models

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Area of Science:

  • Biomechanics
  • Machine Learning
  • Rehabilitation Technology

Background:

  • Low back pain (LBP) is a major global cause of disability.
  • Objective clinical assessment of LBP is limited.
  • Trunk mobility is a key indicator of LBP severity and recovery.

Purpose of the Study:

  • To compare five machine learning models for predicting trunk mobility.
  • To evaluate the effectiveness of inertial sensor data in trunk mobility prediction.
  • To assess the potential for ML-driven objective assessment in LBP management.

Main Methods:

  • Collected trunk mobility data (flexion-extension, rotation, lateralization) from 77 individuals using inertial sensors.
  • Applied data augmentation and normalization techniques.
  • Trained and evaluated LightGBM, XGBoost, HistGradientBoosting, GradientBoosting, and StackingRegressor models.
  • Assessed performance using Mean Absolute Error (MAE), Mean Square Error (MSE), and R2, with statistical significance via ANOVA and Tukey's HSD.

Main Results:

  • GradientBoostingRegressor showed the lowest error and highest statistical significance for flexion-extension and lateralization.
  • StackingRegressor achieved the best performance for rotation prediction.
  • All tested ML models demonstrated the potential to predict trunk mobility from inertial sensor data.

Conclusions:

  • Inertial sensors combined with machine learning provide a viable method for predicting trunk mobility.
  • This approach can facilitate personalized rehabilitation programs for LBP patients.
  • Predictive trunk motion modeling can improve clinical monitoring and reduce socioeconomic burdens associated with LBP.