• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on computational science >Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter?
【24h】

Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter?

机译：通过统计测试和机器学习模型将姿势控制行为与姿势描写区分开来：时间长度是否重要？

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study examines the influence of time series duration on the discriminative power of center-of-pressure (COP) features in distinguishing different population groups via statistical tests and machine learning (ML) models. We used two COP datasets, each containing two groups. One was collected from older adults with low or high risk of falling (dataset Ⅰ), and the other from healthy and post-stroke adults (dataset Ⅱ). Each time series was mapped into a vector of 34 features twice: firstly, using the original duration of 60 s, and then using only the first 30 s. We then compared each feature across groups through traditional statistical tests. Next, we trained six popular ML models to distinguish between the groups using features from the original signals and then from the shorter signals. The performance of each ML model was then compared across groups for the 30 s and 60 s time series. The mean percentage of features able to discriminate the groups via statistical tests was 26.5% smaller for 60 s signals in dataset I, but 13.5% greater in dataset Ⅱ. In terms of ML, better performances were achieved for signals of 60 s in both datasets, mainly for similarity-based algorithms. Hence, we recommend the use of COP time series recorded over at least 60 s. The contribution of this paper also include insights into the robustness of popular ML models to the sampling duration of COP time series.
机译：本研究通过统计检验和机器学习（ML）模型，研究了时间序列持续时间对压力中心（COP）特征的判别力的影响，该特征可用来区分不同的人群。我们使用了两个COP数据集，每个数据集包含两组。一种来自低跌倒风险较高的老年人（数据集Ⅰ），另一种来自健康和中风后的成年人（数据集Ⅱ）。每个时间序列两次映射到具有34个特征的向量中：首先，使用原始时间60 s，然后仅使用前30 s。然后，我们通过传统的统计测试对各个组的每个功能进行了比较。接下来，我们训练了六个流行的ML模型，使用原始信号的特征和较短信号的特征来区分不同的组。然后在30 s和60 s时间序列中比较各组的ML模型的性能。可以通过统计检验辨别组的特征的平均百分比在数据集I中对于60 s信号要小26.5％，而在数据集Ⅱ中要高13.5％。在ML方面，两个数据集中60 s的信号均获得了更好的性能，主要是基于相似度的算法。因此，我们建议使用至少60 s记录的COP时间序列。本文的贡献还包括对流行的ML模型对COP时间序列的采样持续时间的鲁棒性的见解。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号