Understanding and Modeling of WiFi Signal Based Human Activity Recognition

TLDR

Existing WiFi‑based human activity recognition systems lack a quantitative model linking CSI dynamics to activities. This work proposes CARM, a CSI‑based activity recognition and monitoring system. CARM employs CSI‑speed and CSI‑activity models to quantify the relationship between CSI dynamics and human movement, builds activity profiles, and matches observed CSI to the best‑fit profile; it is implemented with commercial WiFi devices and evaluated in multiple environments. CARM achieves an average accuracy exceeding 96 %.

Abstract

Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.