RF-Kinect:A Wearable RFID-based Approach Towards 3D Body Movement Tracking.41:5 Wearable Arm rotation RFID leads to the tag RFID tags displacement antennas and rotation. Fig.2.RF-Kinect illustration of the RFID-based human body movements tracking figure by analyzing the RF signals'reflections through walls and occlusions,thereby accurately locating each part of the human body.Nowadays,as the rapid development of RFID-based localization techniques [28,34,41],more systems are developed to sense human activities based on RFID.Wang et al.[35]recover the moving trace of the tagged finger on a surface plane based on the AoA model.Shangguan et al.[29]tracks the tagged object in the 2D plane for user feedbacks based on only one antenna.But these methods only work in 2D space by tracking a rigid body,and thus are not suitable for tracking the complicated movement of the human body.Lin et al.[21]track the motion status of the tagball based on the phase variation read from the attached tags.Tagyro [39]estimates the orientation of passive objects that have the constant geometric by analyzing the Phase Difference of Arrival of attached tags.Ding et al.[13]aim to detect the fitness gestures leveraging the Doppler profile extracted from the phase trend of RF signals.These RFID-based methods mainly focus on estimating the position/orientation of one single passive rigid body or recognizing the gestures via pattern matching.However,RF-Kinect is designed to track the whole body movement through a model-based approach,which involves several related rigid bodies (i.e.,skeletons)and thus is more challenging for the design of the model-based approach. 3 APPLICATIONS CHALLENGES In this section,we first present the application scenario of RF-Kinect,and introduce the preliminaries of tracking human body movements using RF signals.We then describe the main challenges of the proposed RF-Kinect. 3.1 RF-Kinect Application Scenario The wireless information gathered from wearable RFID tags opens a new research opportunity for developing gesture recognition systems and supporting related applications.RF-Kinect is such a system aiming to track human body movements based on the RF signals emitted from the wearable RFID tags attached to the human body.Taking the Virtual reality(VR)gaming as one example,we can utilize RF-Kinect to recognize the user gestures during the game,in the meanwhile,RF-Kinect can also identify specific users based on the wearable tag IDs at any time.Therefore,RF-Kinect can easily support multi-player games by identifying the users from the tag IDs and automatically reload the gaming process for each user from the tag ID.In the contrast,even traditional vision-based approaches can provide good accuracy in the games,they usually need to manually configure for different users and may also suffer from the interference of surrounding people,leading to bad user experience.Personal fitness,as another example,could also rely on RF-Kinect to associate the recognized activities with the subject for the fitness monitoring.Due to the energy harvesting capability of the wearable RFID tags from backscattered signal,RF-Kinect could operate for a long term without the battery supply issues Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,Vol.2,No.1,Article 41.Publication date:March 2018.RF-Kinect: A Wearable RFID-based Approach Towards 3D Body Movement Tracking • 41:5 RFID antennas Wearable RFID tags Arm rotation leads to the tag displacement and rotation. Fig. 2. RF-Kinect illustration of the RFID-based human body movements tracking. figure by analyzing the RF signals’ reflections through walls and occlusions, thereby accurately locating each part of the human body. Nowadays, as the rapid development of RFID-based localization techniques [28, 34, 41], more systems are developed to sense human activities based on RFID. Wang et al. [35] recover the moving trace of the tagged finger on a surface plane based on the AoA model. Shangguan et al. [29] tracks the tagged object in the 2D plane for user feedbacks based on only one antenna. But these methods only work in 2D space by tracking a rigid body, and thus are not suitable for tracking the complicated movement of the human body. Lin et al. [21] track the motion status of the tagball based on the phase variation read from the attached tags. Tagyro [39] estimates the orientation of passive objects that have the constant geometric by analyzing the Phase Difference of Arrival of attached tags. Ding et al. [13] aim to detect the fitness gestures leveraging the Doppler profile extracted from the phase trend of RF signals. These RFID-based methods mainly focus on estimating the position/orientation of one single passive rigid body or recognizing the gestures via pattern matching. However, RF-Kinect is designed to track the whole body movement through a model-based approach, which involves several related rigid bodies (i.e., skeletons) and thus is more challenging for the design of the model-based approach. 3 APPLICATIONS & CHALLENGES In this section, we first present the application scenario of RF-Kinect, and introduce the preliminaries of tracking human body movements using RF signals. We then describe the main challenges of the proposed RF-Kinect. 3.1 RF-Kinect Application Scenario The wireless information gathered from wearable RFID tags opens a new research opportunity for developing gesture recognition systems and supporting related applications. RF-Kinect is such a system aiming to track human body movements based on the RF signals emitted from the wearable RFID tags attached to the human body. Taking the Virtual reality (VR) gaming as one example, we can utilize RF-Kinect to recognize the user gestures during the game, in the meanwhile, RF-Kinect can also identify specific users based on the wearable tag IDs at any time. Therefore, RF-Kinect can easily support multi-player games by identifying the users from the tag IDs and automatically reload the gaming process for each user from the tag ID. In the contrast, even traditional vision-based approaches can provide good accuracy in the games, they usually need to manually configure for different users and may also suffer from the interference of surrounding people, leading to bad user experience. Personal fitness, as another example, could also rely on RF-Kinect to associate the recognized activities with the subject for the fitness monitoring. Due to the energy harvesting capability of the wearable RFID tags from backscattered signal, RF-Kinect could operate for a long term without the battery supply issues Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, No. 1, Article 41. Publication date: March 2018