Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly

被引：0

作者：

Baldewijns, Greet ^{[1
,2
,3
]}

Luca, Stijn ^{[1
,2
]}

Nagels, William ^{[4
]}

Vanrumste, Bart ^{[1
,2
,3
]}

Croonenborghs, Tom ^{[1
,5
]}

机构：

[1] KU Leuven Technol Campus Geel, AdvISe, Louvain, Belgium

[2] Katholieke Univ Leuven, ESAT STADIUS, Louvain, Belgium

[3] iMinds Med Informat Technol Dept, Louvain, Belgium

[4] Katholieke Univ Leuven, Fac Engn Technol, Louvain, Belgium

[5] Katholieke Univ Leuven, Dept Comp Sci, DTAI, Louvain, Belgium

来源：

2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2015年

关键词：

FALLS;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

引用

页码：5046 / 5049

页数：4

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