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A Wearable RFID System for Real-time Activity Recognition using Radio Patterns. Liang Wang 1 , Tao Gu 2 , Hongwei Xie 1 , Xianping Tao 1 , Jian Lu 1 , and Yu Huang 1 1 State Key Laboratory for Novel Software Technology, Nanjing University, P. R. China.

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a wearable rfid system for real time activity recognition using radio patterns

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

outline
Outline
  • Introduction
  • System Design
  • Evaluation
  • Conclusion & Future Work

State Key Laboratory for Novel Software Technology, Nanjing University

introduction applications
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

introduction motivation
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

introduction motivation1
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

introduction related work
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

introduction our approach
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

introduction our approach1
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

system design
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

preliminary experiment
Preliminary Experiment
  • Potential for activity recognition

C4.5

Recognition accuracy over 95%

State Key Laboratory for Novel Software Technology, Nanjing University

system design1
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

system design2
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

empirical studies
Empirical Studies
  • Data collection
    • 4 volunteers - 8 activities - over 2 weeks

State Key Laboratory for Novel Software Technology, Nanjing University

empirical studies1
Empirical Studies
  • Sliding-window size vs. Recognition accuracy
  • Real-time performance

93.6%

State Key Laboratory for Novel Software Technology, Nanjing University

empirical studies2
Empirical Studies
  • Antenna and tag placement

State Key Laboratory for Novel Software Technology, Nanjing University

empirical studies3
Empirical Studies
  • Transmission power level vs. Recognition accuracy

State Key Laboratory for Novel Software Technology, Nanjing University

conclusion
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