Allahbakhshi Hoda, Hinrichs Timo, Huang Haosheng, Weibel Robert
Geographic Information Systems Unit, Department of Geography, University of Zurich (UZH), Zurich, Switzerland.
Division of Sports and Exercise Medicine, Department of Sports, Exercise and Health, University of Basel, Basel, Switzerland.
Front Physiol. 2019 Feb 12;10:75. doi: 10.3389/fphys.2019.00075. eCollection 2019.
Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.
身体活动（PA）对人类健康和幸福至关重要。然而，关于身体活动的类型以及它们对功能和心理健康以及生活质量产生影响的方式，目前缺乏相关信息。研究已经在受控条件下对身体活动类型进行了测量和分类，但对于现实生活条件下分类器的有效性仅提供了有限的见解。利用类型维度的优势以及现实生活研究设计对身体活动监测的重要性，促使我们针对现实生活条件下的身体活动类型检测（PATD）进行了一项系统的文献综述，重点关注三个主要标准：检测身体活动类型的方法、使用便携式设备收集的加速度计数据以及现实生活场景。在数据库Web of Science、Scopus、PsycINFO和PubMed中进行检索，共识别出1170篇出版物。使用上述选择标准对标题、摘要和全文进行筛选后，本综述纳入了21篇出版物。本综述根据使用现实生活数据集构成PATD过程的三个关键要素进行组织，包括数据收集、预处理和PATD方法。针对这些关键要素提出了建议，特别是关于两个重要的身体活动类别，即姿势和运动活动。现有研究普遍报告了较高到近乎完美的分类准确率。然而，各研究使用的数据收集方案和性能报告方案差异很大，阻碍了跨方法进行透明的性能比较。一般来说，与其他身体活动评估指标（如身体活动强度）相比，关注身体活动类型的研究要少得多，而关注现实生活场景的研究更少。为了在现实生活中可靠地区分基本姿势和运动活动，发现以20Hz采样的两个3D加速度计（大腿和臀部）提供了最小的传感器配置。决策树是实际应用中使用现实生活数据时最常用的分类器。尽管过去一年在现实生活环境中评估身体活动方面取得了重大进展，但要比较各种提出的方法的性能仍然很困难，甚至不可能。因此，迫切需要带有标签、有完整记录且公开可用的参考数据集，包括一个通用的评估框架。
