Evaluation of accelerometer-based fall detection algorithms on real-world falls.
Evaluation of accelerometer-based fall detection algorithms on real-world falls.
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基于加速度计的秋季检测算法评估现实世界跌倒。
DOI：
10.1371/journal.pone.0037062
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Klenk J
中科院分区：
文献类型：
--
作者：
Bagalà F;Becker C;Cappello A;Chiari L;Aminian K;Hausdorff JM;Zijlstra W;Klenk J
尽管进行了广泛的预防工作，跌倒仍然是老年人发病和死亡的一个主要原因。实时检测跌倒并将其紧急发送给远程医疗中心，可以实现快速医疗援助，从而增加老年人的安全感，减少跌倒的一些负面后果。已经探索了许多不同的方法来使用惯性传感器自动检测跌倒。虽然先前发表的算法报告了高灵敏度（SE）和高特异性（SP），但它们通常是在健康志愿者的模拟跌倒中进行的测试。作为SensAction-AAL欧洲项目的一部分，我们最近在现实世界中收集了一些高跌倒风险患者群体的加速度数据。本研究的目的是将13种已发表的跌倒检测算法应用于29个真实跌倒的数据库时，对它们的性能进行基准测试。据我们所知，这是第一次对在真实世界中测试的跌倒检测算法进行系统比较。我们发现13种算法的SP平均值为（mean±std） 83.0%±30.3%（最大值为98%）。SE = 57.0%±27.3%，最大值= 82.8%，明显低于模拟跌落的结果。在对三个具有代表性的瀑布进行的1天监测中，算法产生的虚警次数从3次到85次不等。当已发表的算法应用于现实世界的坠落时，还讨论了影响其性能的因素。这些发现表明了在现实生活条件下测试跌倒检测算法的重要性，以便产生更有效的自动报警系统，并具有更高的接受度。此外，目前的结果支持这样一种观点，即一个大型的、共享的真实世界的跌倒数据库可能会提供对跌倒过程的更好理解，以及设计和评估高性能跌倒探测器所需的信息。
Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.
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影响因子：
3
作者：
Culhane, KM;Lyons, GM;Lyons, D
通讯作者：
Lyons, D
DOI：
10.1109/titb.2005.856864
发表时间：
2006-01-01
影响因子：
--
作者：
Karantonis, DM;Narayanan, MR;Celler, BG
通讯作者：
Celler, BG
影响因子：
2.2
作者：
Klenk, J.;Becker, C.;Lindemann, U.
通讯作者：
Lindemann, U.
影响因子：
4.7
作者：
Doughty, K;Lewis, R;McIntosh, A
通讯作者：
McIntosh, A
影响因子：
4.3
作者：
Hausdorff, JM;Rios, DA;Edelberg, HK
通讯作者：
Edelberg, HK