Analysis of Public Datasets for Wearable Fall Detection Systems.
Analysis of Public Datasets for Wearable Fall Detection Systems.
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DOI：
10.3390/s17071513
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发表时间：
2017-06-27
期刊：
影响因子：
--
通讯作者：
Cano-García JM
中科院分区：
文献类型：
--
作者：
Casilari E;Santoyo-Ramón JA;Cano-García JM
由于智能手表和智能手机等无线手持设备的蓬勃发展，可穿戴式跌倒检测系统（FDS）在过去几年中已成为研究界关注的主要焦点。可穿戴FDS的有效性必须与福尔斯和日常生活活动（ADL）发生期间从惯性传感器获得的各种测量结果进行对比。在这方面，访问公共数据库构成了开放和系统评估跌倒检测技术的基础。本文回顾和评估现有的12个可用的数据存储库，其中包含ADL的测量和模拟福尔斯下降设想的可穿戴FDS的跌倒检测算法的评估。对所发现的数据集的分析是以综合的方式进行的，考虑到为生成移动性样本而部署的测试台的定义中涉及的多个因素。痕迹的研究揭示了缺乏一个共同的实验基准程序，因此，从许多角度（样本的长度和数量，模拟福尔斯和ADL的类型，测试对象的特征，传感器的特征和位置等）的数据集的大异质性。关于这一点，样本的统计分析揭示了传感器范围对轨迹可靠性的影响。此外，该研究还证明了选择ADL的重要性以及根据运动强度对ADL进行分类的必要性，以便评估某种检测算法区分福尔斯和ADL的能力。
Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.
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