A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait
A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait
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DOI：
10.1016/j.gaitpost.2022.05.015
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发表时间：
2022-05-18
期刊：
影响因子：
2.4
通讯作者：
Falla, Deborah
中科院分区：
文献类型：
--
作者：
Jimenez-Grande, David;Atashzar, S. Farokh;Falla, Deborah
背景：步态特征的变化已被报道在慢性颈痛（CNP）的人。研究问题：我们可以通过使用基于惯性测量单元（IMU）的步态运动学数据训练机器学习模型来对有CNP和没有CNP的人进行分类吗？研究方法：本研究招募了18名无症状个体和21名CNP受试者，并进行了两种步态轨迹，（1）头部伸直的直线行走（单任务）和（2）头部连续旋转的直线行走（双任务）。运动学数据记录从三个IMU传感器连接到前额，上胸椎（T1），下胸椎（T12）。提取时间和频谱特征，以生成单任务和双任务步态的数据集。为了评估最重要的特征并同时减少数据集大小，使用了邻域成分分析（NCA）方法。三个监督模型，包括K-最近邻，支持向量机和线性判别分析，以测试最重要的时间和光谱特征的性能。结果：NCA实施后，所有分类器的性能都有所提高。在双任务步态中，仅使用9个特征，NCA-支持向量机的准确率为86.85%，特异性为83.30%，灵敏度为92.85%。重要性：研究结果提出了一种数据驱动的方法和基于机器学习的方法，用于从步态过程中获得的高维数据中识别测试条件和特征，用于对有CNP和无CNP的人进行分类。
Background: Changes in gait characteristics have been reported in people with chronic neck pain (CNP). Research question: Can we classify people with and without CNP by training machine learning models with Inertial Measurement Units (IMU)-based gait kinematic data? Methods: Eighteen asymptomatic individuals and 21 participants with CNP were recruited for the study and performed two gait trajectories, (1) linear walking with their head straight (single-task) and (2) linear walking with continuous head-rotation (dual-task). Kinematic data were recorded from three IMU sensors attached to the forehead, upper thoracic spine (T1), and lower thoracic spine (T12). Temporal and spectral features were extracted to generate the dataset for both single-and dual-task gait. To evaluate the most significant features and simultaneously reduce the dataset size, the Neighbourhood Component Analysis (NCA) method was utilized. Three supervised models were applied, including K-Nearest Neighbour, Support Vector Machine, and Linear Discriminant Analysis to test the performance of the most important temporal and spectral features. Results: The performance of all classifiers increased after the implementation of NCA. The best performance was achieved by NCA-Support Vector Machine with an accuracy of 86.85%, specificity of 83.30%, and sensitivity of 92.85% during the dual-task gait using only nine features. Significance: The results present a data-driven approach and machine learning-based methods to identify test conditions and features from high-dimensional data obtained during gait for the classification of people with and without CNP.