Acceleration Signal Categorization Using Support Vector Machines

被引：4

作者：

Davis, B. T. ^{[1
]}

Caicedo, J. M. ^{[2
]}

Hirth, V. A. ^{[3
]}

Easterling, B. M. ^{[4
]}

机构：

[1] Adv Smart Syst & Evaluat Technol LLC, 1400 Laurel St,Suite 1B, Columbia, SC 29201 USA

[2] Univ South Carolina, Dept Civil & Environm Engn, SDII Lab, 300 Main St, Columbia, SC 29201 USA

[3] Palmetto Hlth USC Med Grp, Senior Care Primary Practice, 3010 Farrow Rd Suite 300, Columbia, SC 29203 USA

[4] William Jennings Bryan Dorn Vet Adm Med Ctr, 6439 Garners Ferry Rd, Columbia, SC 29209 USA

来源：

EXPERIMENTAL TECHNIQUES | 2019年 / 43卷 / 03期

基金：

美国国家科学基金会;

关键词：

Acceleration signal; Support vector machine; NaN density; Maximum amplitude difference ratio; Rate of dispersion; Dispersion ratio;

D O I：

10.1007/s40799-019-00318-y

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Large sensor installations are becoming prominent as the cost of sensors drop and new methods are developed for structural health monitoring, fall detection, building occupancy, etc. Large amounts of data could be quickly captured, especially for measurements of high sampling rate such as acceleration signals. Methods to quickly triage records for further analysis can be used to drastically reduce the amount of data to be process. This paper studies the use of Support Vector Machines to classify floor vibration signals to determine signals of interest. Four kernels and three signal metrics were explored in this research using a human activity dataset containing over 500,000 acceleration records. Results show that the Radial Basis Function using a Dispersion Ratio metric can be used to identify signals of interest effectively.

引用

页码：359 / 368

页数：10

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