Abstract
Much work have been done in activity recognition using wearable sensors organized in a body sensor network. The quality and communication reliability of the sensor data much affects the system performance. Recent studies show the potential of using RFID radio information instead of sensor data for activity recognition. This approach has the advantages of low cost and high reliability. Radio-based recognition method is also amiable to packet loss and has the advantages including MAC layer simplicity and low transmission power level. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system using passive tags which are smaller and more cost-effective to recognize human activities in real-time. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address two issues - the false negative issue of tag readings and tag/antenna calibration, and design a fast online recognition system. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 % with a latency of 5 s.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use the Electronic Product Code (EPC) stored on a tag as its ID.
References
Natarajan, A., de Silva, B., Yap, K.-K., Motani, M.: Link layer behavior of body area networks at 2.4 GHz. In: Proceedings of ACM Annual International Conference Mobile on Computing and Networking (MobiCom), pp. 241–252 (2009)
Qi, X., Zhou, G., Li, Y., Peng, G.: Radiosense: exploiting wireless communication patterns for body sensor network activity recognition. In: Proceedings of IEEE Real-Time Systems Symposium (RTSS), pp. 95–104 (2012)
Wagner, S., Handte, M., Zuniga, M., Marrón, P.J.: Enhancing the performance of indoor localization using multiple steady tags. Pervasive Mob. Comput. 9(3), 392–405 (2013)
Zhang, D., Zhou, J., Guo, M., Cao, J., Li, T.: TASA: tag-free activity sensing using RFID tag arrays. IEEE Trans. Parallel Distrib. Syst. (TPDS) 22(4), 558–570 (2011)
Asadzadeh, P., Kulik, L., Tanin, E.: Gesture recognition using RFID technology. Pers. Ubiquit. Comput. 16(3), 225–234 (2012)
Gu, T., Wang, L., Wu, Z., Tao, X., Lu, J.: A pattern mining approach to sensor-based human activity recognition. IEEE Trans. Knowl. Data Eng. (TKDE) 23(9), 1359–1372 (2010)
Peng, Q., Zhang, C., Song, Y., Wang, Z., Wang, Z.: A low-cost, low-power UHF RFID reader transceiver for mobile applications. In: Radio Frequency Integrated Circuits Symposium (RFIC), pp. 243–246 (2012)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Quwaider, M., Biswas, S.: Body posture identification using hidden Markov model with a wearable sensor network. In: Proceedings of International Conference on Body Area Networks, p. 19 (2008)
Kuo, S.M., Lee, B.H., Tian, W.: Real-Time Digital Signal Processing: Implementations and Applications. Wiley, Chichester (2006)
Acknowledgement
This work was supported by the National 863 project under Grant 2013AA01A213 and the NSFC under Grants 91318301, 61373011, 61073031, the program B for Outstanding PhD candidate of NJU under Grant 201301B016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, L., Gu, T., Xie, H., Tao, X., Lu, J., Huang, Y. (2014). A Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-11569-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11568-9
Online ISBN: 978-3-319-11569-6
eBook Packages: Computer ScienceComputer Science (R0)