A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.
A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.
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DOI：
10.3389/fnagi.2021.692865
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发表时间：
2021
影响因子：
4.8
通讯作者：
Xiong S
中科院分区：
文献类型：
--
作者：
Yu X;Jang J;Xiong S
基于可穿戴惯性传感器的预碰撞碰撞检测（在人体与地面碰撞之前检测碰撞事故）研究在过去十年中发展迅速，因为它具有开发按需跌倒相关伤害预防系统的巨大潜力。然而，大多数研究人员使用自己的数据集来开发跌倒检测算法，很少公开这些数据集，这对在共同基础上公平评估不同算法的性能提出了挑战。尽管最近已经建立了一些开放的数据集，但由于缺乏坠落时间的时间标签和有限的运动类型，大多数数据集对于撞击前的坠落检测是不切实际的。为了克服这些限制，在本研究中，我们提出并公开提供了一个名为“KFall”的大规模运动数据集，该数据集由32名韩国参与者开发，他们在腰背上佩戴惯性传感器，进行21种日常生活活动和15种模拟跌倒。此外，基于同步运动视频的准备使用的坠落时间时间标签与数据集一起发布。这些增强使KFall成为第一个适合于碰撞前跌落检测的公共数据集，而不仅仅是跌落后检测。重要的是，我们还开发了三种不同类型的最新算法（基于阈值，支持向量机和深度学习），使用KFall数据集进行预碰撞跌倒检测，以便研究人员和从业者可以灵活选择相应的算法。深度学习算法在预碰撞跌落检测中具有较高的总体准确率和平衡的灵敏度（99.32%）和特异性（99.01%）。支持向量机的灵敏度为99.77%，特异度为94.87%。然而，基于阈值的算法结果相对较差，特别是特异性（83.43%）远低于敏感性（95.50%）。这些算法的性能可以被视为一个基准，进一步开发更好的算法与这个新的数据集。这种大规模的运动数据集和基准算法可以为研究人员和从业人员提供有价值的数据和参考，为开发老年人碰撞前跌倒检测和主动损伤预防的新技术和新策略提供参考。
Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.
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DOI：
10.1115/1.4043449
发表时间：
2019-08-01
影响因子：
1.7
作者：
Kim, Tae Hyong;Choi, Ahnryul;Mun, Joung Hwan
通讯作者：
Mun, Joung Hwan
碰撞前跌倒检测
影响因子：
3.9
作者：
Hu X;Qu X
通讯作者：
Qu X
DOI：
10.3390/s17071513
发表时间：
2017-06-27
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Casilari E;Santoyo-Ramón JA;Cano-García JM
通讯作者：
Cano-García JM
影响因子：
2.4
作者：
Kangas, M.;Vikman, I.;Jamsa, T.
通讯作者：
Jamsa, T.
影响因子：
3.2
作者：
Aziz, Omar;Musngi, Magnus;Robinovitch, Stephen N.
通讯作者：
Robinovitch, Stephen N.