RF-Motion: A Device-Free RF-Based Human Motion Recognition System

Abstract

In recent years, human motion recognition, as an important application of the intelligent perception of the Internet of Things, has received extensive attention. Many applications benefit from motion recognition, such as motion monitoring, elderly fall detection, and somatosensory games. Several existing RF-based motion recognition systems are susceptible to multipath effects in complex environments, resulting in lower recognition accuracy and difficulty in extending to other scenarios. To address this challenge, we propose RF-Motion, a device-free commercial off-the-shelf (COTS) RFID-based human motion recognition system that can detect human motion in complex multipath environments such as indoor environments. And when the environment changes, RF-Motion still has high recognition accuracy, even without retraining. In addition, we use data slicing to solve the problem of discontinuity in the time domain of RFID communication and then use the synthetic aperture (SAR) algorithm to obtain the fingerprint feature matrix corresponding to each motion. Finally, the dynamic time warping (DTW) algorithm is used to match the prior motion fingerprint database to complete the motion recognition. Experiments show that RF-Motion can achieve up to 90% accuracy for human motion recognition in an indoor environment, and when the environment changes, it can still reach a minimum accuracy of 87%.