This article has been accepted for inclusion in a future issue of this journal.Content is final as presented,with the exception of pagination IEEE/ACM TRANSACTIONS ON NETWORKING is 3.7cm,which means that the accuracy of tracking two REID fingers could be 7.4cm,but finger movements are typically 2cm to 5cm.Furthermore,the deployment cost of RF-IDraw RFID is relatively expensive as it requires an antenna array of eight Antenna antennas and two RFID readers.Similarly,RFID localization eration schemes do not work well for recognizing concurrent multi- ane finger micromovements because the location accuracy is not Hands with multiple enough.For example,the state-of-the-art localization schemes RFID tags PinIt achieves an accuracy of 16cm at 90 percentile [11]. and Tagoram achieves an accuracy with a median error dis- tance of 6.35cm [12].In summary,the above RFID-based localization schemes,including RF-IDraw,are not suitable for micromovement recognition,as they mainly focus on the absolute tag positioning rather than the relative movement pattern of multiple tags.As a matter of fact,to achieve more accurate performance in the micromovement recognition,we should focus on the phase variation pattern caused by the micromovement of multiple fingers,instead of capturing the location variation of multiple fingers,since the former metric captures the micromovement in much more fine granularity than the latter. (b) Fig.2.System Overview.(a)Antenna deployment.(b)Tag deployment. C.Proposed Approach In this paper,we propose RF-Glove,a concurrent multi- finger micromovement recognition system based on RF sig- In other words,each different type of multi-finger micromove- nals.RF-Glove uses a commercial off-the-shelf(COTS)RFID ments can be characterized by different RF phase variations reader with 3 antennas and five EPCglobal C1G2 standard Thus,by capturing the distinguishing RF phase variation passive tags attached to the five fingers of a glove,one patterns,we can recognize different multi-finger micromove- tag per finger.The three antennas form two antenna pairs, ments.Our RF-micromovement model fundamentally explains which are placed in a mutually orthogonal manner on a flat why multi-finger micromovements can be recognized based on plane.Fig.2 shows the overview of our system with the 3 phase variations from RF signals. antennas deployed on the office ceiling.In performing multi- finger micromovements,we let the RFID reader continuously D.Technical Challenges and Solutions interrogate these tags and obtain the backscattered RF signals There are several technical challenges we need to address in from each tag.For each antenna-tag pair,the reader obtains a this paper.The first challenge is to properly tradeoff between sequence of RF phase values called a phase profile.For each accuracy and robustness in terms of matching resolution. type of multi-finger micromovement,we obtain a set of 3x 5 Given a testing set of phase profiles and a few template phase profiles.Given the phase profile set of a testing multi- sets of phase profiles for the micromovement,we need to finger micromovement,we compare the corresponding set of find the template set that the testing set matches the best. phase profiles with the templates of each type of multi-finger If the matching resolution is too high,then the matching micromovement to find the most similar template. robustness is too low due to the inherent unstableness in multi- To understand how RF signals vary with multi-finger micro- finger micromovements.If the matching resolution is too low, movements,in this paper,we propose a 3D positioning model then the matching accuracy is too low due to the inherent and a RF-micromovement model,respectively,to depict the common characteristics among different types of multi-finger relationship between the multi-finger movement and the RF- micromovements.To address this challenge,we propose a signals.Specifically,to recognize the large range movement, two-phase approach to this matching problem.In this first such as the swipe and punch,and locate the position of phase,we perform a coarse-grained filtering to identify some the hand during the small-range micromovement,such as the template sets that the testing set should not be matched to. zoom in/out and flick,we propose a 3D positioning model by referring to the moving status of the fingers and the to continuously locate the tags'positions for further micro- variation trend of the phase profile.In the second phase, movement recognition.To depict the relationship between we perform a fine-grained matching to match the testing set the phase variation and the multi-finger micromovement, to one of the remaining template sets,by referring to the we propose a RF-micromovement model that quantifies the details of phase profiles with time warping.Thus,we can use relationship between RF signals and micromovements.Our different matching resolutions to tradeoff between accuracy RF-micromovement model shows that RF phase variations and robustness. and multi-finger micromovement present a linear relationship, The second challenge is to tackle the variation of tem- when it is performed in the central beam of the antenna.plate phase profiles at different positions.It is observed thatThis article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 IEEE/ACM TRANSACTIONS ON NETWORKING is 3.7cm, which means that the accuracy of tracking two fingers could be 7.4cm, but finger movements are typically 2cm to 5cm. Furthermore, the deployment cost of RF-IDraw is relatively expensive as it requires an antenna array of eight antennas and two RFID readers. Similarly, RFID localization schemes do not work well for recognizing concurrent multi- finger micromovements because the location accuracy is not enough. For example, the state-of-the-art localization schemes PinIt achieves an accuracy of 16cm at 90 percentile [11], and Tagoram achieves an accuracy with a median error dis￾tance of 6.35cm [12]. In summary, the above RFID-based localization schemes, including RF-IDraw, are not suitable for micromovement recognition, as they mainly focus on the absolute tag positioning rather than the relative movement pattern of multiple tags. As a matter of fact, to achieve more accurate performance in the micromovement recognition, we should focus on the phase variation pattern caused by the micromovement of multiple fingers, instead of capturing the location variation of multiple fingers, since the former metric captures the micromovement in much more fine granularity than the latter. C. Proposed Approach In this paper, we propose RF-Glove, a concurrent multi- finger micromovement recognition system based on RF sig￾nals. RF-Glove uses a commercial off-the-shelf (COTS) RFID reader with 3 antennas and five EPCglobal C1G2 standard passive tags attached to the five fingers of a glove, one tag per finger. The three antennas form two antenna pairs, which are placed in a mutually orthogonal manner on a flat plane. Fig. 2 shows the overview of our system with the 3 antennas deployed on the office ceiling. In performing multi- finger micromovements, we let the RFID reader continuously interrogate these tags and obtain the backscattered RF signals from each tag. For each antenna-tag pair, the reader obtains a sequence of RF phase values called a phase profile. For each type of multi-finger micromovement, we obtain a set of 3 × 5 phase profiles. Given the phase profile set of a testing multi- finger micromovement, we compare the corresponding set of phase profiles with the templates of each type of multi-finger micromovement to find the most similar template. To understand how RF signals vary with multi-finger micro￾movements, in this paper, we propose a 3D positioning model and a RF-micromovement model, respectively, to depict the relationship between the multi-finger movement and the RF￾signals. Specifically, to recognize the large range movement, such as the swipe and punch, and locate the position of the hand during the small-range micromovement, such as the zoom in/out and flick, we propose a 3D positioning model to continuously locate the tags’ positions for further micro￾movement recognition. To depict the relationship between the phase variation and the multi-finger micromovement, we propose a RF-micromovement model that quantifies the relationship between RF signals and micromovements. Our RF-micromovement model shows that RF phase variations and multi-finger micromovement present a linear relationship, when it is performed in the central beam of the antenna. Fig. 2. System Overview. (a) Antenna deployment. (b) Tag deployment. In other words, each different type of multi-finger micromove￾ments can be characterized by different RF phase variations. Thus, by capturing the distinguishing RF phase variation patterns, we can recognize different multi-finger micromove￾ments. Our RF-micromovement model fundamentally explains why multi-finger micromovements can be recognized based on phase variations from RF signals. D. Technical Challenges and Solutions There are several technical challenges we need to address in this paper. The first challenge is to properly tradeoff between accuracy and robustness in terms of matching resolution. Given a testing set of phase profiles and a few template sets of phase profiles for the micromovement, we need to find the template set that the testing set matches the best. If the matching resolution is too high, then the matching robustness is too low due to the inherent unstableness in multi- finger micromovements. If the matching resolution is too low, then the matching accuracy is too low due to the inherent common characteristics among different types of multi-finger micromovements. To address this challenge, we propose a two-phase approach to this matching problem. In this first phase, we perform a coarse-grained filtering to identify some template sets that the testing set should not be matched to, by referring to the moving status of the fingers and the variation trend of the phase profile. In the second phase, we perform a fine-grained matching to match the testing set to one of the remaining template sets, by referring to the details of phase profiles with time warping. Thus, we can use different matching resolutions to tradeoff between accuracy and robustness. The second challenge is to tackle the variation of tem￾plate phase profiles at different positions. It is observed that