Yu Xiaoqun, Jang Jaehyuk, Xiong Shuping
Department of Industrial and Systems Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Front Aging Neurosci. 2021 Jul 16;13:692865. doi: 10.3389/fnagi.2021.692865. eCollection 2021.
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.
在过去十年中，利用可穿戴惯性传感器进行撞击前跌倒检测（在身体与地面撞击之前检测跌倒事故）的研究迅速发展，因为其在开发按需跌倒相关伤害预防系统方面具有巨大潜力。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，很少将这些数据集公开，这给在共同基础上公平评估不同算法的性能带来了挑战。尽管最近已经建立了一些开放数据集，但由于缺乏跌倒时间的时间标签以及运动类型有限，其中大多数对于撞击前跌倒检测并不实用。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名韩国参与者在低腰佩戴惯性传感器并进行21种日常生活活动和15种模拟跌倒时生成。此外，基于同步运动视频的跌倒时间的即用型时间标签与数据集一起发布。这些改进使KFall成为第一个不仅适用于跌倒后检测，还适用于撞击前跌倒检测的公共数据集。重要的是，我们还开发了三种不同类型的最新算法（基于阈值的、支持向量机和深度学习），使用KFall数据集进行撞击前跌倒检测，以便研究人员和从业者可以灵活选择相应的算法。深度学习算法在撞击前跌倒检测中实现了较高的总体准确率以及平衡的灵敏度（99.32%）和特异性（99.01%）。支持向量机也表现出良好的性能，灵敏度为99.77%，特异性为94.87%。然而，基于阈值的算法显示出相对较差的结果，尤其是特异性（83.43%）远低于灵敏度（95.50%）。这些算法的性能可被视为利用这个新数据集进一步开发更好算法的基准。这个大规模运动数据集和基准算法可以为研究人员和从业者提供有价值的数据和参考，以开发用于撞击前跌倒检测和老年人主动伤害预防的新技术和策略。
