41 RF-Kinect:A Wearable RFID-based Approach Towards 3D Body Movement Tracking CHUYU WANG,Nanjing University,CHN JIAN LIU,Rutgers University,USA YINGYING CHEN',Rutgers University,USA LEI XIE',Nanjing University,CHN HONGBO LIU,Indiana University-Purdue University Indianapolis,USA SANGLU LU,Nanjing University,CHN The rising popularity of electronic devices with gesture recognition capabilities makes the gesture-based human-computer interaction more attractive.Along this direction,tracking the body movement in 3D space is desirable to further facilitate behavior recognition in various scenarios.Existing solutions attempt to track the body movement based on computer version or wearable sensors,but they are either dependent on the light or incurring high energy consumption.This paper presents RF-Kinect,a training-free system which tracks the body movement in 3D space by analyzing the phase information of wearable RFID tags attached on the limb.Instead of locating each tag independently in 3D space to recover the body postures,RF-Kinect treats each limb as a whole,and estimates the corresponding orientations through extracting two types of phase features, Phase Difference between Tags(PDT)on the same part of a limb and Phase Difference between Antennas(PDA)of the same tag It then reconstructs the body posture based on the determined orientation of limbs grounded on the human body geometric model,and exploits Kalman filter to smooth the body movement results,which is the temporal sequence of the body postures. The real experiments with 5 volunteers show that RF-Kinect achieves 8.7 angle error for determining the orientation of limbs and 4.4cm relative position error for the position estimation of joints compared with Kinect 2.0 testbed. CCS Concepts:.Networks-Sensor networks;Mobile networks;.Human-centered computing-Mobile devices; Additional Key Words and Phrases:RFID;Body movement tracking ACM Reference Format: Chuyu Wang.Jian Liu,Yingying Chen,Lei Xie,Hongbo Liu,and Sanglu Lu.2018.RF-Kinect:A Wearable RFID-based Approach Towards 3D Body Movement Tracking.Proc.ACM Interact.Mob.Wearable Ubiguitous Technol.2,1,Article 41(March 2018). 28 pages.https://doi.org/10.1145/3191773 "Yingying Chen and Lei Xie are the co-corresponding authors,Email:yingche@scarletmail rutgers.edu,Ixie@nju.edu.cn Authors'addresses:Chuyu Wang.Nanjing University,State Key Laboratory for Novel Software Technology,163 Xianlin Ave,Nanjing,210046, CHN:Jian Liu,Rutgers University,Department of Electrical and Computer Engineering,North Brunswick,NJ,08902,USA;Yingying Chen, Rutgers University,Department of Electrical and Computer Engineering,North Brunswick,NJ,08902,USA;Lei Xie,Nanjing University,State Key Laboratory for Novel Software Technology,163 Xianlin Ave,Nanjing.210046,CHN:Hongbo Liu,Indiana University-Purdue University Indianapolis.Department of Computer,Information and Technology.Indianapolis,IN,46202,USA:Sanglu Lu,Nanjing University,State Key Laboratory for Novel Software Technology,163 Xianlin Ave,Nanjing,210046,CHN. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is permitted.To copy otherwise,or republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.Request permissions from permissions@acm.org. 2018 Association for Computing Machinery. 2474-9567/2018/3-ART41$15.00 https:/doi.org/10.1145/3191773 Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,Vol.2,No.1,Article 41.Publication date:March 2018.41 RF-Kinect: A Wearable RFID-based Approach Towards 3D Body Movement Tracking CHUYU WANG, Nanjing University, CHN JIAN LIU, Rutgers University, USA YINGYING CHEN∗ , Rutgers University, USA LEI XIE∗ , Nanjing University, CHN HONGBO LIU, Indiana University-Purdue University Indianapolis, USA SANGLU LU, Nanjing University, CHN The rising popularity of electronic devices with gesture recognition capabilities makes the gesture-based human-computer interaction more attractive. Along this direction, tracking the body movement in 3D space is desirable to further facilitate behavior recognition in various scenarios. Existing solutions attempt to track the body movement based on computer version or wearable sensors, but they are either dependent on the light or incurring high energy consumption. This paper presents RF-Kinect, a training-free system which tracks the body movement in 3D space by analyzing the phase information of wearable RFID tags attached on the limb. Instead of locating each tag independently in 3D space to recover the body postures, RF-Kinect treats each limb as a whole, and estimates the corresponding orientations through extracting two types of phase features, Phase Difference between Tags (PDT) on the same part of a limb and Phase Difference between Antennas (PDA) of the same tag. It then reconstructs the body posture based on the determined orientation of limbs grounded on the human body geometric model, and exploits Kalman filter to smooth the body movement results, which is the temporal sequence of the body postures. The real experiments with 5 volunteers show that RF-Kinect achieves 8.7 ◦ angle error for determining the orientation of limbs and 4.4cm relative position error for the position estimation of joints compared with Kinect 2.0 testbed. CCS Concepts: • Networks → Sensor networks; Mobile networks; • Human-centered computing → Mobile devices; Additional Key Words and Phrases: RFID; Body movement tracking ACM Reference Format: Chuyu Wang, Jian Liu, Yingying Chen, Lei Xie, Hongbo Liu, and Sanglu Lu. 2018. RF-Kinect: A Wearable RFID-based Approach Towards 3D Body Movement Tracking. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 41 (March 2018), 28 pages. https://doi.org/10.1145/3191773 ∗Yingying Chen and Lei Xie are the co-corresponding authors, Email: yingche@scarletmail.rutgers.edu, lxie@nju.edu.cn. Authors’ addresses: Chuyu Wang, Nanjing University, State Key Laboratory for Novel Software Technology, 163 Xianlin Ave, Nanjing, 210046, CHN; Jian Liu, Rutgers University, Department of Electrical and Computer Engineering, North Brunswick, NJ, 08902, USA; Yingying Chen, Rutgers University, Department of Electrical and Computer Engineering, North Brunswick, NJ, 08902, USA; Lei Xie, Nanjing University, State Key Laboratory for Novel Software Technology, 163 Xianlin Ave, Nanjing, 210046, CHN; Hongbo Liu, Indiana University-Purdue University Indianapolis, Department of Computer, Information and Technology, Indianapolis, IN, 46202, USA; Sanglu Lu, Nanjing University, State Key Laboratory for Novel Software Technology, 163 Xianlin Ave, Nanjing, 210046, CHN. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 Association for Computing Machinery. 2474-9567/2018/3-ART41 $15.00 https://doi.org/10.1145/3191773 Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, No. 1, Article 41. Publication date: March 2018