Exploring the Influence of Feature Selection Methods on a Random Forest Model for Gait Time Series Prediction Using Inertial Measurement Units.

IF 1.7 4区 医学 Q4 BIOPHYSICS
Shima Mohammadi Moghadam, Julie Choisne
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引用次数: 0

Abstract

Despite the increasing use of inertial measurement units (IMUs) and machine learning techniques for gait analysis, there remains a gap in which feature selection methods are best tailored for gait time series prediction. This study explores the impact of using various feature selection methods on the performance of a random forest (RF) model in predicting lower limb joints kinematics from two IMUs. The primary objectives of this study are as follows: (1) Comparing eight feature selection methods based on their ability to identify more robust feature sets, time efficiency, and impact on RF models' performance, and (2) assessing the performance of RF models using generalized feature sets on a new dataset. Twenty-three typically developed (TD) children (ages 6-15) participated in data collection involving optical motion capture (OMC) and IMUs. Joint kinematics were computed using opensim. By employing eight feature selection methods (four filter and four embedded methods), the study identified 30 important features for each target. These selected features were used to develop personalized and generalized RF models to predict lower limbs joints kinematics during gait. This study reveals that various feature selection methods have a minimal impact on the performance of personalized and generalized RF models. However, the RF and mutual information (MI) methods provided slightly lower errors and outliers. MI demonstrated remarkable robustness by consistently identifying the most common features across different participants. ElasticNet emerged as the fastest method. Overall, the study illuminated the robustness of RF models in predicting joint kinematics during gait in children, showcasing consistent performance across various feature selection methods.

探索特征选择方法对随机森林模型步态时间序列预测的影响。
尽管越来越多地使用imu和机器学习技术进行步态分析,但仍然存在一个空白,即最适合步态时间序列预测的特征选择方法。本研究探讨了使用各种特征选择方法对随机森林(RF)模型从两个imu预测下肢关节运动学的性能的影响。本研究的主要目标是:1)比较八种特征选择方法,基于它们识别更健壮的特征集的能力、时间效率和对射频模型的影响?2)在新数据集上使用广义特征集评估RF模型的性能。23名典型发育儿童(6至15岁)参与了包括光学运动捕捉和imu在内的数据收集。使用OpenSim计算关节运动学。通过采用8种特征选择方法(4种滤波方法和4种嵌入方法),确定了每个目标的30个重要特征。这些选定的特征被用于开发个性化和广义RF模型,以预测步态期间的下肢关节运动学。该研究表明,各种特征选择方法对个性化和广义射频模型的性能影响最小。然而,RF和互信息(MI)方法提供了稍低的误差和异常值。通过一致地识别不同参与者的最常见特征,MI展示了显著的鲁棒性。ElasticNet成为最快的方法。总体而言,该研究阐明了RF模型在预测儿童步态期间关节运动学方面的鲁棒性,展示了各种特征选择方法的一致性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.40
自引率
5.90%
发文量
169
审稿时长
4-8 weeks
期刊介绍: Artificial Organs and Prostheses; Bioinstrumentation and Measurements; Bioheat Transfer; Biomaterials; Biomechanics; Bioprocess Engineering; Cellular Mechanics; Design and Control of Biological Systems; Physiological Systems.
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