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加速度计检测日常活动的最佳位置。

Optimal placement of accelerometers for the detection of everyday activities.

机构信息

School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.

出版信息

Sensors (Basel). 2013 Jul 17;13(7):9183-200. doi: 10.3390/s130709183.

DOI:10.3390/s130709183
PMID:23867744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3758644/
Abstract

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

摘要

本文旨在探讨如何确定加速度计的最佳位置,以实现对各种日常活动的检测。本研究调查了将放置在不同身体部位的加速度计数据进行组合对活动检测准确性的影响。八位健康男性参与了本研究。数据由六个无线三轴加速度计采集,这些加速度计分别放置在胸部、手腕、下背部、臀部、大腿和脚部。所进行的活动包括行走、在电动跑步机上跑步、坐、躺、站立和上下楼梯。在所有研究的机器学习算法中,支持向量机提供了最准确的活动检测。尽管所有位置的数据提供了相似的准确性水平,但臀部是使用支持向量机进行活动检测的最佳单个位置,其准确性比其他研究位置略高。使用一个以上的传感器可显著提高分类的准确性。使用两个或更多传感器时,准确性没有显著差异。然而,应当注意,在尝试检测更精细的活动时,使用单个或多个加速度计进行活动检测的差异可能更为明显。因此,未来的工作将进一步研究加速度计位置对更广泛的这些活动的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/8ec2277829b3/sensors-13-09183f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/5d4911d0cb0d/sensors-13-09183f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/6216986f9f03/sensors-13-09183f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/8ec2277829b3/sensors-13-09183f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/5d4911d0cb0d/sensors-13-09183f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/6216986f9f03/sensors-13-09183f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a581/3758644/8ec2277829b3/sensors-13-09183f3.jpg

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