Effects of Housing Environments on Human Activity Recognition using Fine-grained WiFi Signatures

Monitoring human activities in daily living (ADL) using fine-grained Wi-Fi signal signatures has the great potential to support an intelligent eldercare system in a home environment. Such an approach analyzes Channel State... [ view full abstract ]

Monitoring human activities in daily living (ADL) using fine-grained Wi-Fi signal signatures has the great potential to support an intelligent eldercare system in a home environment. Such an approach analyzes Channel State Information (CSI) extracted from a sequence of Wi-Fi packets and identifies its temporal variations as a fingerprint, caused by human activities. However, the performance of the Wi-Fi-based ADL recognition can be greatly affected by the physical surrounding environment due to the way that the radio wave is propagated, i.e., penetrated or reflected by an obstacle. In particular, building materials are expected to bring a significant impact on its performance. In this context, this paper aims to examine the effect of physical housing environment on the performance of Wi-Fi-based ADL recognition. ADL recognition systems were implemented at two different housing environments: a wood-frame apartment and a reinforced-concrete-frame apartment, which represent typical housing environments in United States and Korea, respectively. The experimental results indicate that building structural materials combined with other environmental factors (i.e., housing density) creates a significant difference in the accuracy rate of Wi-Fi-based ADL recognition and provide an insight on how such systems should be configured for homes.