RT Journal Article
T1 Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments
A1 Fernández-Carmona, Manuel
A1 Mghames, Sariah
A1 Bellotto, Nicola
K1 Ondículas
K1 Entropía
K1 Internet de los objetos
AB 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.
PB IOS Press
YR 2024
FD 2024
LK https://hdl.handle.net/10630/34193
UL https://hdl.handle.net/10630/34193
LA eng
NO https://openpolicyfinder.jisc.ac.uk/id/publication/2005
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026