Periodic Behavioral Routine Discovery Based on Implicit Spatial Correlations for Smart Home

Abstract

As the degree of elders’ social activity and self-care ability depreciates, the potential risk for elderly people who live independently increases. The development of assistive services such as smart homes could likely provide them with a safer living environment. These systems collect sensor data to monitor residents’ daily activities and provide assistance services accordingly. In order to do so, a smart home must understand its residents’ daily activities and identify their periodic behavioral daily routine accordingly. However, existing solutions mainly focus on the temporal feature of daily activities and require prior labeling of where sensors are geographically deployed. In this study, we extract implicit spatial information from hidden correlations between sensors deployed in the environment and present a concept of virtual locations that establishes an abstract spatial representation of the physical living space so that prior labeling of the actual location of the sensors is not required. To demonstrate the viability of this concept, an unsupervised periodic behavioral routine discovery method that does not require any predefined location-specific sensor data for a smart home environment is proposed. The experimental results show that with the help of virtual location, the proposed method achieves high accuracy in activity discovery and significantly reduces the computation time required to complete the task relative to a system without virtual location. Furthermore, the result of simulated anomaly detection also shows that the periodic behavioral routine discovery system is more tolerant to differences in the way routines are performed.