Falling Angel - A Wrist Worn Fall Detection System Using K-NN Algorithm

被引：1

作者：

Rahman, Hamidur ^{[1
]}

Sandberg, Johan ^{[1
]}

Eriksson, Lennart ^{[1
]}

Heidari, Mohammad ^{[1
]}

Arwald, Jan ^{[2
]}

Eriksson, Peter ^{[2
]}

Begum, Shahina ^{[1
]}

Linden, Maria ^{[1
]}

Ahmed, Mobyen Uddin ^{[1
]}

机构：

[1] Malardalen Univ, Sch Innovat Design & Engn, Vasteras, Sweden

[2] Exformat AB, Lidingo, Sweden

来源：

INTERNET OF THINGS TECHNOLOGIES FOR HEALTHCARE, HEALTHYIOT 2016 | 2016年 / 187卷

关键词：

Fall detection; Angel device; k-Nearest Neighbor;

D O I：

10.1007/978-3-319-51234-1_25

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A wrist worn fall detection system has been developed where the accelerometer data from an angel sensor is analyzed by a two-layered algorithm in an android phone. Here, the first layer uses a threshold to find potential falls and if the thresholds are met, then in the second layer a machine learning i.e., k-Nearest Neighbor (k-NN) algorithm analyses the data to differentiate it from Activities of Daily Living (ADL) in order to filter out false positives. The final result of this project using the k-NN algorithm provides a classification sensitivity of 96.4%. Here, the acquired sensitivity is 88.1% for the fall detection and the specificity for ADL is 98.1%.

引用

页码：148 / 151

页数：4

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