Anomaly Detection in Activities of Daily Living Using One-Class Support Vector Machine

被引：8

作者：

Yahaya, Salisu Wada ^{[1
]}

Langensiepen, Caroline ^{[1
]}

Lotfi, Ahmad ^{[1
]}

机构：

[1] Nottingham Trent Univ, Sch Sci & Technol, Nottingham NG11 8NS, England

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI) | 2019年 / 840卷

关键词：

Novelty detection; Anomaly detection; One-class SVM; Activities of Daily Living (ADL);

D O I：

10.1007/978-3-319-97982-3_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Different computational methodologies for anomaly detection has been studied in the past. Novelty detection involves classifying if test data differs from the training data. This is applicable to a scenario when there are sufficiently many normal training samples and little or no abnormal data. In this research, a novelty detection algorithm known as One-Class Support Vector Machine (SVM) is applied for detection of anomaly in Activities of Daily Living (ADL), specifically sleeping patterns, which could be a sign of Mild Cognitive Impairment (MCI) in older adults or other health-related issues. Tests conducted on both synthetic and real data shows promising results.

引用

页码：362 / 371

页数：10

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