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跌倒检测边缘推理的最佳位置

Optimal Location for Fall Detection Edge Inferencing.

作者信息

Paolini Christopher, Soselia Davit, Baweja Harsimran, Sarkar Mahasweta

机构信息

Department of Electrical and Computer Engineering San Diego State University San Diego, California USA.

Department of Electrical and Computer Engineering San Diego State University Tbilisi, Georgia.

出版信息

IEEE Glob Commun Conf. 2019 Dec;2019. doi: 10.1109/globecom38437.2019.9014212.

Abstract

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling onto a hard surface. Approximately 32-42% of those 70 and over fall at least once each year, and those who live in assisted living facilities fall with greater frequency per year than those who live in residential communities. Delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in concussion, traumatic brain injury, or bone fracture. Several implementations of mobile, wireless, wearable, low-power fall detection sensors (FDS) have become commercially available. These devices are typically worn around the neck as a pendant, or on the wrist, as a watch is worn. Based on features collected from IMU sensors placed at sixteen body locations, and used to train four different machine learning models, our findings show the optimal placement for an FDS on the body is in front of the shinbone.

摘要

老年人身体受伤的一个主要原因是意外摔倒在坚硬表面上。70岁及以上的人群中,约32% - 42%的人每年至少摔倒一次,且居住在辅助生活设施中的人每年摔倒的频率高于居住在住宅社区的人。如果摔倒导致脑震荡、创伤性脑损伤或骨折,摔倒后至就医的延迟会加重损伤。几种移动、无线、可穿戴、低功耗的跌倒检测传感器(FDS)已在市场上推出。这些设备通常像吊坠一样戴在脖子上,或者像戴手表一样戴在手腕上。基于从放置在身体16个部位的惯性测量单元(IMU)传感器收集的特征,并用于训练四种不同的机器学习模型,我们的研究结果表明,FDS在身体上的最佳放置位置是胫骨前方。

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本文引用的文献

1
Deaths from Falls Among Persons Aged ≥65 Years - United States, 2007-2016.
MMWR Morb Mortal Wkly Rep. 2018 May 11;67(18):509-514. doi: 10.15585/mmwr.mm6718a1.
3
Improving Fall Detection Using an On-Wrist Wearable Accelerometer.
Sensors (Basel). 2018 Apr 26;18(5):1350. doi: 10.3390/s18051350.
4
Evaluation of accelerometer-based fall detection algorithms on real-world falls.
PLoS One. 2012;7(5):e37062. doi: 10.1371/journal.pone.0037062. Epub 2012 May 16.
5
Fall detection--principles and methods.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:1663-6. doi: 10.1109/IEMBS.2007.4352627.
6
Falls in older people: epidemiology, risk factors and strategies for prevention.
Age Ageing. 2006 Sep;35 Suppl 2:ii37-ii41. doi: 10.1093/ageing/afl084.
7
Index for rating diagnostic tests.
Cancer. 1950 Jan;3(1):32-5. doi: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3.
8
Incidence and costs of unintentional falls in older people in the United Kingdom.
J Epidemiol Community Health. 2003 Sep;57(9):740-4. doi: 10.1136/jech.57.9.740.
9
Long short-term memory.
Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735.
10
Persons found in their homes helpless or dead.
N Engl J Med. 1996 Jun 27;334(26):1710-6. doi: 10.1056/NEJM199606273342606.

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