Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor
Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor
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DOI：
10.1177/2055668319868544
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发表时间：
2019-08-01
影响因子：
2
通讯作者：
Caulfield, Brian
中科院分区：
文献类型：
--
作者：
Argent, Rob;Drummond, Sean;Caulfield, Brian
关节角度测量是康复治疗中一个重要的客观指标。惯性测量单元可以提供关节角度评估的准确且可靠的方法。本研究的目的是评估应用机器学习算法的单个传感器是否可以准确测量髋关节和膝关节角度，并研究惯性测量单元定位算法和个人特定变量对准确性的影响。方法14名健康受试者完成8次康复训练，运动学数据由3D运动捕捉系统采集，作为参考标准，并使用可穿戴惯性测量单元。使用四种机器学习模型从单个惯性测量单元计算关节角度，并与参考标准进行比较以评估准确度。结果所有练习中表现最好的算法的平均均方根误差为4.81度（SD = 1.89）。使用惯性测量单元定向算法作为预处理步骤提高了准确性;然而，添加个人特定变量增加了误差，平均RMSE为4.99度（SD = 1.83度）。结论使用机器学习可以从单个惯性测量单元以良好的精度测量髋关节和膝关节角度。这提供了在诊所外使用单个传感器监测和记录动态关节角度的能力。
Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81 degrees (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99 degrees (SD = 1.83 degrees). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.