利用可穿戴传感器和机器学习客观估算 m-CTSIB 平衡测试得分

Marjan Nassajpour, Mustafa Shuqair, A. Rosenfeld, M. Tolea, James E. Galvin, Behnaz Ghoraani
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摘要

在医疗保健领域,准确的平衡评估对于识别和管理影响稳定性和协调性的疾病非常重要。它在预防跌倒、了解运动障碍和设计适当的治疗干预措施方面发挥着关键作用,适用于不同年龄组和不同病症。然而,传统的平衡评估方法往往存在主观性、缺乏全面的平衡评估和远程评估功能、依赖于专业设备和专家分析等问题。为了应对这些挑战,我们的研究引入了一种创新方法,用于估算改良临床平衡感觉交互测试(m-CTSIB)的得分。利用可穿戴传感器和先进的机器学习算法,我们提供了一种客观、易用且高效的平衡评估方法。我们使用惯性测量单元(IMU)传感器阵列和专用系统收集了 34 名参与者在四种不同感官条件下的综合运动数据,以评估 m-CTSIB 平衡评分的基本真实值,供我们进行分析。然后对这些数据进行预处理,并提取大量特征进行分析。为了估算 m-CTSIB 分数,我们采用了多元线性回归 (MLR)、支持向量回归 (SVR) 和 XGBOOST 算法。我们针对不同主题进行的 "一出一进 "和五倍交叉验证分析表明,我们的方法具有很高的准确性,而且与地面实况的平衡评分有很强的相关性,从而验证了我们方法的有效性和可靠性。我们还获得了有关特定动作、特征选择和传感器位置在平衡估算中的重要性的重要启示。值得注意的是,利用腰部传感器数据的 XGBOOST 模型在这两种方法中都取得了优异的成绩,其中 "留空 "交叉验证的相关性为 0.96,平均绝对误差(MAE)为 0.23;5 倍交叉验证的相关性为 0.92,平均绝对误差为 0.23,结果与之相当,证实了该模型的一致性能。这一发现凸显了我们的方法彻底改变平衡评估实践的潜力,尤其是在传统方法不实用或无法使用的情况下。
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Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning
Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model’s consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.
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