Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
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DOI：
10.3390/s20123600
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发表时间：
2020-06-01
期刊：
影响因子：
3.9
通讯作者：
Rashedi, Ehsan
中科院分区：
文献类型：
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作者：
Abdollahi, Masoud;Ashouri, Sajad;Rashedi, Ehsan
非特异性腰痛（NSLBP）是一种严重的健康挑战，影响着全球数百万人的健康和社会经济后果。在今天的临床环境中，从业者继续遵循传统的指导方针，根据主观方法对NSLBP患者进行分类，例如STartT回筛工具（SBST）。本研究旨在开发一种基于传感器的机器学习模型，根据定量运动学数据将NSLBP患者分为不同的亚组，即，躯干运动和平衡相关的措施，结合STartT输出。具体而言，惯性测量单元（IMU）被连接到94例患者的躯干，而他们进行重复的躯干屈曲/伸展运动的平衡板上，在自选的步伐。机器学习算法（支持向量机（SVM）和多层感知器（MLP））被用于模型开发，SBST结果被用作基础事实。结果表明，运动学数据可以成功地用于将患者分为两个主要组：高风险与低-中等风险。SVM和MLP分别达到了75%和60%的准确度。另外，在本文详述的一系列变量中，时间缩放的IMU信号产生最高的准确度水平（即，75%）。我们的研究结果支持可穿戴系统在开发各种医疗保健应用的诊断和预后工具方面的改进和使用。这可以促进在临床和家庭环境中开发改进的、具有成本效益的定量NSLBP评估工具，以实现有效的个性化康复。
Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today's clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of similar to 75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., similar to 75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.