IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain.
School of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, Ecuador.
Sensors (Basel). 2024 Jan 27;24(3):831. doi: 10.3390/s24030831.
Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura-Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP.
下背痛（LBP）是一种常见的肌肉骨骼疾病，也是导致工作缺勤的主要原因。本项目旨在开发一种医学测试，以帮助医疗保健专业人员为非特异性 LBP 患者确定和分配物理治疗。该设计使用基于运动捕捉（MoCap）数据分类的机器学习（ML）模型，这些数据是从厄瓜多尔因巴布拉的健康和临床诊断为 LBP 的患者的运动范围（ROM）练习中获得的。为了评估和比较，测试了以下七种 ML 算法：逻辑回归、决策树、随机森林、支持向量机（SVM）、k-最近邻（KNN）、多层感知机（MLP）和梯度提升算法。所有 ML 技术的准确率均高于 80%，其中三个模型（SVM、随机森林和 MLP）的准确率高于 90%。研究发现 SVM 是表现最好的算法。本文旨在通过使用精确的时空测量和数据驱动的治疗方法来提高惯性 MoCap 在医疗保健中的适用性，从而提高慢性 LBP 患者的生活质量。
