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探索特征选择方法对随机森林模型的影响,用于使用惯性测量单位进行步行时间序列预测
Shima Mohammadi Moghadam1, Julie Choisne1
1Auckland Bioengineering Institute, The University of Auckland, 70 Symonds Street, Level 8, Auckland 1010, New Zealand.
Journal of biomechanical engineering
|February 8, 2025
概括
特征选择方法对随机森林模型性能的影响最小,用于使用惯性测量单位预测儿童步行动力学. 相互信息和随机森林方法显示,步态分析的结果略有改善.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 步行分析通常使用惯性测量单位 (IMU) 和机器学习.
- 时间序列步态预测的最佳特征选择仍未得到充分探索.
研究的目的:
- 比较八种特征选择方法的稳定性,效率和对随机森林 (RF) 模型性能的影响.
- 在一个新的数据集上评估具有概括特征的射频模型,用于预测下肢关节动力学.
主要方法:
- 收集了23名典型发育儿童 (6-15岁) 的光学运动捕捉 (OMC) 和IMU数据.
- 使用opensim计算的关节动力学.
- 采用了八种特征选择方法 (四种过器,四种嵌入式) 来识别每个目标的30个特征.
- 开发了个性化和通用的射频模型用于步态预测.
主要成果:
- 特性选择方法对个性化和通用的射频模型性能影响最小.
- 随机森林 (RF) 和相互信息 (MI) 方法的误差和异常值略低.
- 相互信息 (MI) 始终确定了参与者之间的共同特征.
- 弹性网是最快的功能选择方法.
结论:
- 随机森林 (RF) 模型在预测儿童步行关节动力学方面表现出强度.
- 在各种特征选择技术中观察到一致的性能,突出显示了RF模型在儿科步态分析中的适应性.
相关概念视频
Wald-Wolfowitz Runs Test I
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
Wald-Wolfowitz Runs Test II
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...

