The Impacts of CSI Temporal Variations on CSI-based Occupancy Monitoring Systems: An Exploratory Study.

Channel State Information (CSI) has been used for an alternative sensing source for occupancy monitoring systems to classify activities of daily living (ADLs). Previous studies have proposed learning-based activity classification models, which require similar distributions of CSI for training and testing datasets. However, as CSI varies even in a static environment, the activity classification model trained with data collected in a particular day would be invalid for other time frames. In this context, this study examines the impacts of the CSI temporal variations on the learning-based occupant activity monitoring systems. An experiment was performed to collect the CSI data while an occupant performed daily activities for six days. Three learning-based activity classification models reconstructed from the previous studies were trained and tested with time-dependent cross validation. The performances of the benchmark models were greatly degraded (below 60%) with testing data collected at different days than the training data, while their performances with testing data collected at the same day with training data were over 90%. This study also explores the opportunity to address this issue with transfer learning techniques.