A consensus novelty detection ensemble approach for anomaly detection in activities of daily living

A new approach to creating an ensemble of novelty detection algorithms is proposed in this paper. The novelty detection process identifies new or unknown data by detecting if a test data differs significantly from the data available during training. It is applicable for anomaly detection in a situation where there is sufficiently large training data representing the normal class and little or no training data for the anomalous (abnormal) class. Abnormality in Activities of Daily Living (ADL) is identified as any significant deviation from an individual’s usual behavioural routine. Novelty detection is relevant to ADL anomaly detection since abnormalities in ADL are rare and data representing the anomalous cases are not readily available. The proposed Consensus Novelty Detection Ensemble approach is based on the concept of internal and external consensus. The internal consensus is an internal voting scheme within each model in the ensemble while the external consensus is an external voting scheme among the ensemble models. The weight of each model is estimated based on its performance and a score, called “Normality Score”. Computed score is used in classifying the data as abnormal (anomalous) based on certain threshold crossing, normal otherwise. Experimental evaluation is conducted to detect abnormalities in ADL data obtained from CASAS repository as well as experimental dataset collected for this research. The obtained results show that the proposed approach is able to identify anomalous instances. The proposed approach offers more flexibility compared with the existing approaches by allowing the Normality Score threshold to be adjusted without retraining the models