Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments
This work addresses anomaly detection for smart-home and robot-assisted living, but it appears incremental as it builds on existing methods like Markov Logic Networks with wavelet extensions.
The paper tackled the problem of detecting anomalies in sensor data for human activity monitoring in smart robot-assisted environments by developing a wavelet-based temporal model integrated with Hybrid Markov Logic Networks, achieving effective deployment in office and domestic scenarios with new datasets.
Abstract. Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.