Feature engineering workflow for activity recognition from synchronized inertial measurement units
Feature engineering workflow for activity recognition from synchronized inertial measurement units
复制标题
用于从同步惯性测量单元进行活动识别的特征工程工作流程
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Besier
中科院分区：
文献类型：
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作者：
A. Kempa;J. Oram;Andrew Wong;M. Finch;T. Besier
无处不在的可穿戴传感器推动了物联网的发展，同时也对体育科学和精准医学产生了影响。虽然从智能手机数据或其他类型的惯性测量单元（IMU）中识别人类活动已经发展成为机器学习最突出的日常生活示例之一，但时间序列特征工程的底层过程似乎仍然很耗时。这个漫长的过程阻碍了基于imu的机器学习在运动科学和精准医学中的应用的发展。本文讨论了一个特征工程工作流，该工作流基于FRESH算法（基于可扩展假设测试的特征提取）自动提取时间序列特征，以识别同步IMU传感器（IMeasureU Ltd， NZ）的统计显著特征。特征工程工作流程有五个主要步骤：时间序列工程、自动时间序列特征提取、优化特征提取、专门分类器的拟合和优化机器学习管道的部署。讨论了用户特定的跑走分类的工作流程，并演示了其推广到多用户多活动分类的过程。
The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.
影响因子：
6
作者：
Christ, Maximilian;Braun, Nils;Kempa-Liehr, Andreas W.
通讯作者：
Kempa-Liehr, Andreas W.