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

Abstract

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.