A Wearable RFID System for Real-time Activity Recognition using Radio Patterns

1. A Wearable RFID System for Real-time Activity Recognition using Radio Patterns Liang Wang1, Tao Gu2, Hongwei Xie1, Xianping Tao1, Jian Lu1, and Yu Huang1 1State Key Laboratory for Novel Software Technology, Nanjing University, P. R. China. {wl,xhw}@smail.nju.edu.cn,{txp,lj,yuhuang}@nju.edu.cn 2 School of Computer Science and Information Technology, RMIT University, Australia. tao.gu@rmit.edu.au

2. Outline • Introduction • System Design • Evaluation • Conclusion & Future Work State Key Laboratory for Novel Software Technology, Nanjing University

3. Introduction - Applications • Recognizing people’s activities continuously in real-time enables a wide range of applications, e.g., Health Monitoring Emergency Response Entertainment Assisted Living State Key Laboratory for Novel Software Technology, Nanjing University

4. Introduction - Motivation • Traditionally, a body sensor network (BSN) is used to capture activity data Recognition Algorithms A BSN-based activity recognition system Wearable sensors Wireless communication Processing unit State Key Laboratory for Novel Software Technology, Nanjing University

5. Introduction – Motivation • Limitations of BSNs • Human body affects the wireless link quality • Sensing, computing, storage, communication devices • Battery powered Packet loss State Key Laboratory for Novel Software Technology, Nanjing University

6. Introduction – Related Work • Passive RFID systems for localization [1] and gesture recognition [2] • RSS patterns for localization and gesture recognition • Advantages: cost-efficient, reliable, battery-free • Limitations: fixed-reader & simple activities only • Recent work on wearabe 2.4G network for human activity recognition [3] • Radio patterns for activity recognition • Advantages: energy-efficient, amiable to packet loss • Limitations: traditional BSN nodes [1] S. Wagner, M. Handte, M. Zuniga, and P. J. Marron, “Enhancing the Performance of Indoor localization Using Multiple Steady Tags,” Pervasive and Mobile Computing, vol. 9, no. 3, pp. 392–405, 2013. [2] P. Asadzadeh, L. Kulik, and E. Tanin, “Gesture Recognition Using RFID Technology,” Personal and Ubiquitous Computing, vol. 16, no. 3, pp. 225–234, 2012. [3] X. Qi, G. Zhou, Y. Li, and G. Peng, “Radiosense: Exploiting Wireless Communication Patterns for Body Sensor Network Activity Recognition,” in Proc. IEEE Real-Time Systems Symposium (RTSS), pp. 95–104, 2012. State Key Laboratory for Novel Software Technology, Nanjing University

7. Introduction – Our Approach • Two observations • There exists heavy attenuation of the human body to radio communication band in which the UHF RFID operates • RFID radio communication is highly affected by the tag-antenna distance and orientation • Intuition Blockage of line-of-sight Activities Tag-antenna distance & orientation Tag 1: RSS … Radio Patterns Recognition … Tag N: RSS … Passive tag UHF RFID reader State Key Laboratory for Novel Software Technology, Nanjing University

8. Introduction – Our Approach • Research Issues • How to discriminate different activities from the RFID radio patterns? • How to perform real-time activity recognition? • Challenges • False negative readings - a tag is in the antenna’s reading range, but not detected; our current RFID reader can activate one antenna at a time. • Behavior difference - readings from different combinations of tags and antennas may be different even with the same condition. State Key Laboratory for Novel Software Technology, Nanjing University

9. System Design • Antenna / Tag Placement • 36 tags • 9 body parts: both wrists, arms, legs, ankles, and the body • 4 tags for each body part: reliable reading • 4 antennas • Detecting hand/arm movements: chest, back • Detecting lower body movements: left feet, right feet • Reading the tags • 2 seconds for each antenna • 8 seconds to complete a reading cycle State Key Laboratory for Novel Software Technology, Nanjing University

10. Preliminary Experiment • Potential for activity recognition C4.5 Recognition accuracy over 95% State Key Laboratory for Novel Software Technology, Nanjing University

11. System Design • Data segmentation • Fixed sliding-window of L seconds • L is the application specific recognition delay bound • Data completion – False negative readings Temporal locality – tags recently detected are likely to be detected again with similar RSS values Last Window Current Window Current Data Completed Data Ant 0: Ant 1: Ant 2: Combine Data Ant 3: Time State Key Laboratory for Novel Software Technology, Nanjing University

12. System Design • Feature extraction – Behavior difference • Temporal features • mean, variance, max, min, mean crossing rate, frequency domain energy, and entropy of the RSS values for each pair of tag and antenna separately • Spatial features • the correlation coefficients of RSS series for different tags read by different antennas • Real-time recognition algorithm • Online: recognition based on existing data • Continuous: processing time < data collection time, i.e., L • Solution: fixed sliding-window + SVM State Key Laboratory for Novel Software Technology, Nanjing University

13. Empirical Studies • Data collection • 4 volunteers - 8 activities - over 2 weeks State Key Laboratory for Novel Software Technology, Nanjing University

14. Empirical Studies • Sliding-window size vs. Recognition accuracy • Real-time performance 93.6% State Key Laboratory for Novel Software Technology, Nanjing University

15. Empirical Studies • Antenna and tag placement State Key Laboratory for Novel Software Technology, Nanjing University

16. Empirical Studies • Transmission power level vs. Recognition accuracy State Key Laboratory for Novel Software Technology, Nanjing University

17. Conclusion • We present in this paper • Wearable UHF RFID-based recognition system • Real-time recognition algorithm • Future work • Better sensing device • Mobile phone integrated RFID reader • More sensitive reader • Better deployment strategy • The minimal number of antennas and tags needed • More empirical studies • More activities • More users State Key Laboratory for Novel Software Technology, Nanjing University

18. Thank you! Q&A