SGRS: A sequential gesture recognition system using COTS RFID

Gesture recognition is an innovative technology which is fundamentally reshaping the way people live, entertain and work. However, most gesture recognition systems focus on the recognition of simple gestures and ignore the full potential of sequential gestures involving a series of temporally-related simple actions in order. This paper presents SGRS, a battery-free, scalable and non-specific sequential gesture recognition system based on COTS RFID. The key insight is that finegrained phase information extracted from RF signals is capable of perceiving various gestures. In SGRS, we meticulously devise gesture recognition mechanism by incorporating the k-means based vector quantizer and string matching algorithm to enable precise and real-time sequential gesture identification. Moreover, an improved edit distance algorithm is proposed for suppressing individual diversity. We implement SGRS and comprehensively evaluate the performance by recognizing traffic command gestures of Chinese traffic police. Experimental result shows that SGRS achieves an average recognition accuracy of 96.2% with eight sequential gestures and is highly robust to both individual diversity and multipath effect.

[1]  Kaishun Wu,et al.  GRfid: A Device-Free RFID-Based Gesture Recognition System , 2017, IEEE Transactions on Mobile Computing.

[2]  Lei Yang,et al.  TagBooth: Deep shopping data acquisition powered by RFID tags , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[3]  He Wang,et al.  I am a Smartwatch and I can Track my User's Arm , 2016, MobiSys.

[4]  Xiang Cao,et al.  Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework , 2017, IEEE Journal of Biomedical and Health Informatics.

[5]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[6]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[8]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[9]  Yu-Chee Tseng,et al.  Calorie Map: An Activity Intensity Monitoring System Based on Wireless Signals , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[10]  Jin-Hyung Kim,et al.  An HMM-Based Threshold Model Approach for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  K. V. S. Rao,et al.  Phase based spatial identification of UHF RFID tags , 2010, 2010 IEEE International Conference on RFID (IEEE RFID 2010).

[12]  Seong-Whan Lee,et al.  Gesture Spotting in Continuous Whole Body Action Sequences Using Discrete Hidden Markov Models , 2005, Gesture Workshop.

[13]  Paulo André da Silva Gonçalves,et al.  Enhancing the efficiency of active RFID-based indoor location systems , 2009, WCNC.

[14]  Raul I. Ramos-Garcia,et al.  Improving the Recognition of Eating Gestures Using Intergesture Sequential Dependencies , 2015, IEEE Journal of Biomedical and Health Informatics.

[15]  Jane Labadin,et al.  Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).

[16]  Kai-Ten Feng,et al.  Particle-Based Window Rotation and Scaling Scheme for Real-Time Hand Recognition and Tracking , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[17]  Zimu Zhou,et al.  Enabling Gesture-based Interactions with Objects , 2017, MobiSys.

[18]  Paul Lukowicz,et al.  Wearable Activity Tracking in Car Manufacturing , 2008, IEEE Pervasive Computing.

[19]  Laurie Davies,et al.  The identification of multiple outliers , 1993 .

[20]  Wei Xi,et al.  RFIPad: Enabling Cost-Efficient and Device-Free In-air Handwriting Using Passive Tags , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).