Wavelet-based Temporal Forecasting Models of Human Activities for Anomaly Detection.

This paper presents a novel approach for temporal modelling of long-term human activities based on wavelet transforms. The model is applied to binary smart-home sensors to forecast their signals, which are used then as temporal priors to infer anomalies in office and Active & Assisted Living (AAL) scenarios. Such inference is performed by a new extension of Hybrid Markov Logic Networks (HMLNs) that merges different anomaly indicators, including activity levels detected by sensors, expert rules and the new temporal models. The latter in particular allow the inference system to discover deviations from long-term activity patterns, which cannot by detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the wavelet-based temporal models and their application to signal forecasting and anomaly detection. The experimental results show the effectiveness of the proposed techniques and their successful application to detect unexpected activities in office and AAL settings.