Anomaly detection in activities of daily living using one-class support vector machine

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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.

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Yahaya, S. W., Langensiepen, C., & Lotfi, A. (2019). Anomaly detection in activities of daily living using one-class support vector machine. In Advances in Intelligent Systems and Computing (Vol. 840, pp. 362–371). Springer Verlag. https://doi.org/10.1007/978-3-319-97982-3_30

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