Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

Villalba-Meneses, Fernando; Guevara, Cesar; Lojan, Alejandro B.; Gualsaqui, Mario G.; Arias-Serrano, Isaac; Velásquez-López, Paolo A.; Almeida-Galárraga, Diego; Tirado-Espín, Andrés; Marín, Javier; Marín, José J.

doi:10.3390/s24030831

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…For instance, it has been reported that the prediction of factors helps improve the prognosis of ACL reconstruction using ML models such as ElasticNet and SVM [26]. Villalba-Meneses et al described the classification using ML models such as SVM of motion capture data obtained from the range of motion exercises among healthy and clinically diagnosed patients with low back pain [27]. Kusunose et al indicated that the integration of MediaPipe with ML models using liner regression and LightGBM for posture analysis has improved accuracy in assessing shoulder abduction angles [20].…”

Section: Figure 15: Conversion From Rotational Displacement Coordinat...mentioning

confidence: 99%

Estimating the Thumb Rotation Angle by Using a Tablet Device With a Posture Estimation Artificial Intelligence Model

Ehara,

Inui,

Mifune

et al. 2024

Cureus

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MediaPipe Hand (MediaPipe) is an artificial intelligence (AI)-based pose estimation library. In this study, MediaPipe was combined with four machine learning (ML) models to estimate the rotation angle of the thumb. Videos of the right hands of 15 healthy volunteers were recorded and processed into 9000 images. The rotation angle of the thumb (defined as angle θ from the palmar plane, which is defined as 0°) was measured using an angle measuring device, expressed in a radian system. Angle θ was then estimated by the ML model by using parameters calculated from the hand coordinates detected by MediaPipe. The linear regression model showed a root mean square error (RMSE) of 12.23, a mean absolute error (MAE) of 9.9, and a correlation coefficient of 0.91. The ElasticNet model showed an RMSE of 12.23, an MAE of 9.95, and a correlation coefficient of 0.91; the support vector machine (SVM) model showed an RMSE of 4.7, an MAE of 2.5, and a correlation coefficient of 0.99. The LightGBM model achieved high values: an RMSE of 4.58, an MAE of 2.62, and a correlation coefficient of 0.99. Based on these findings, we concluded that the thumb rotation angle can be estimated with high accuracy by combining MediaPipe and ML.

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Section: Figure 15: Conversion From Rotational Displacement Coordinat...mentioning

confidence: 99%

Estimating the Thumb Rotation Angle by Using a Tablet Device With a Posture Estimation Artificial Intelligence Model

Ehara,

Inui,

Mifune

et al. 2024

Cureus

View full text Add to dashboard Cite

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Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

A. Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

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This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.

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Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

Cited by 2 publications

References 38 publications

Estimating the Thumb Rotation Angle by Using a Tablet Device With a Posture Estimation Artificial Intelligence Model

Estimating the Thumb Rotation Angle by Using a Tablet Device With a Posture Estimation Artificial Intelligence Model

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

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