41:8·C.Wang et al.. or moving antennas to locate a target tag in 2D environment.Other related studies such as [27]and [29]can locate a tagged object with two antennas or only one antenna,but they only work in 2D plane and they only track one object by attaching one or more tags on it.For the complex body movement in 3D space,it is not applicable to the motion tracking in our application scenario.Thus,a dual-antenna-based solution needs to be proposed to facilitate the 3D body movement tracking Imperfect Phase Measurements.Unlike previous RFID-based localization studies [35,41]that track the tag movement in 2D space,our work aims to achieve a more challenging goal,i.e.,tracking the movement in 3D space.So it poses even higher requirements on the phase measurements.There are multiple factors that may affect the uniqueness and accuracy of phase measurements related to the body movement.According to our preliminary study,the phase change of the RF signal is determined by both the tag-antenna distance and the tag orientation.Moreover,both the water-rich human body and the muscle deformation during the body movement may also affect the phase measurements from RF signals.All the above factors together make it much harder to track the human body movement in 3D space leveraging the phase information in RF-signals Training-free Body Movement Tracking.Existing studies on gesture tracking usually spend significant efforts on training the classification model by asking the users to perform each specific gesture multiple times [13, 25].However,the number of gestures that can be recognized highly relies on the size of the training set,so the scalability to unknown gestures is greatly thwarted.Some other methods [29,31,35]are designed to recover the trace of a rigid body(e.g.,the finger and box)from the signal models,but they are not suitable for the complex human body,which consists of several rigid bodies.In order to identify diverse gestures or postures flexibly of the complex human body,it is critical to develop a body movement tracking system that does not rely on any training dataset. 4 SYSTEM DESIGN In this section,we first introduce the architecture of our RF-Kinect system,and then present the building modules of RF-Kinect for tracking the 3D body movement. 4.1 System Architecture The basic idea of RF-Kinect is to derive the body posture in each scanning round by analyzing the RF signals from the wearable RFID tags attached on the limbs and chest,and then reconstruct the body movement from a series of body postures in consecutive scans.Figure 5 illustrates the architecture of RF-Kinect.We first extract the phase information of M RFID tags from two antennas in consecutive scanning rounds as Phase Stream,where all the attached tags are read in each scanning round.Then the system is initialized by requiring the user to stand still with his/her arms hanging down naturally.As a perfect rigid object,the tags on the chest enable Body Position/Orientation Estimation module to determine the position and facing orientation of the user relative to the antennas based on a model-based approach in the previous work(e.g.,[21]).Then,Coordinate Transformation module converts the relative positions of the antennas into the Skeleton Coordinate System(SCS),which is defined based on the human body geometric structure in Section 4.2,so that the coordinates of both the tags and antennas could be expressed properly.Based on the coordinates of the antennas and tags attached on the user body when the user stands still,the theoretical phase value of each tag is calculated from Eq.(1).Phase Deviation Elimination module then computes the phase offset between the theoretical and the measured phase value,which is used to eliminate the phase deviation in the following biased phase stream. After the above preprocessing,Phase Difference Extraction module extracts two phase related features from the RF signal measurements in each scanning round:(i)Phase Difference between any two Tags(PDT)attached to the same part of a limb(e.g.,the upper arm),and (ii)Phase Difference between the two Antennas(PDA)of the same tag.The two phase related features are then utilized to estimate the limb postures based on the 3D Limb Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,Vol.2,No.1,Article 41.Publication date:March 2018.41:8 • C. Wang et al. or moving antennas to locate a target tag in 2D environment. Other related studies such as [27] and [29] can locate a tagged object with two antennas or only one antenna, but they only work in 2D plane and they only track one object by attaching one or more tags on it. For the complex body movement in 3D space, it is not applicable to the motion tracking in our application scenario. Thus, a dual-antenna-based solution needs to be proposed to facilitate the 3D body movement tracking. Imperfect Phase Measurements. Unlike previous RFID-based localization studies [35, 41] that track the tag movement in 2D space, our work aims to achieve a more challenging goal, i.e., tracking the movement in 3D space. So it poses even higher requirements on the phase measurements. There are multiple factors that may affect the uniqueness and accuracy of phase measurements related to the body movement. According to our preliminary study, the phase change of the RF signal is determined by both the tag-antenna distance and the tag orientation. Moreover, both the water-rich human body and the muscle deformation during the body movement may also affect the phase measurements from RF signals. All the above factors together make it much harder to track the human body movement in 3D space leveraging the phase information in RF-signals. Training-free Body Movement Tracking. Existing studies on gesture tracking usually spend significant efforts on training the classification model by asking the users to perform each specific gesture multiple times [13, 25]. However, the number of gestures that can be recognized highly relies on the size of the training set, so the scalability to unknown gestures is greatly thwarted. Some other methods [29, 31, 35] are designed to recover the trace of a rigid body (e.g., the finger and box) from the signal models, but they are not suitable for the complex human body, which consists of several rigid bodies. In order to identify diverse gestures or postures flexibly of the complex human body, it is critical to develop a body movement tracking system that does not rely on any training dataset. 4 SYSTEM DESIGN In this section, we first introduce the architecture of our RF-Kinect system, and then present the building modules of RF-Kinect for tracking the 3D body movement. 4.1 System Architecture The basic idea of RF-Kinect is to derive the body posture in each scanning round by analyzing the RF signals from the wearable RFID tags attached on the limbs and chest, and then reconstruct the body movement from a series of body postures in consecutive scans. Figure 5 illustrates the architecture of RF-Kinect. We first extract the phase information of M RFID tags from two antennas in consecutive scanning rounds as Phase Stream, where all the attached tags are read in each scanning round. Then the system is initialized by requiring the user to stand still with his/her arms hanging down naturally. As a perfect rigid object, the tags on the chest enable Body Position/Orientation Estimation module to determine the position and facing orientation of the user relative to the antennas based on a model-based approach in the previous work (e.g., [21]). Then, Coordinate Transformation module converts the relative positions of the antennas into the Skeleton Coordinate System (SCS), which is defined based on the human body geometric structure in Section 4.2, so that the coordinates of both the tags and antennas could be expressed properly. Based on the coordinates of the antennas and tags attached on the user body when the user stands still, the theoretical phase value of each tag is calculated from Eq. (1). Phase Deviation Elimination module then computes the phase offset between the theoretical and the measured phase value, which is used to eliminate the phase deviation in the following biased phase stream. After the above preprocessing, Phase Difference Extraction module extracts two phase related features from the RF signal measurements in each scanning round: (i) Phase Difference between any two Tags (PDT) attached to the same part of a limb (e.g., the upper arm), and (ii) Phase Difference between the two Antennas (PDA) of the same tag. The two phase related features are then utilized to estimate the limb postures based on the 3D Limb Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, No. 1, Article 41. Publication date: March 2018