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Identifying RFID-
embedded objects in
pervasive healthcare
    applications



                       1
Reference

 Yu-Ju Tu, Wei Zhou, Selwyn Piramuthu “Identifying
 RFID-embedded objects in pervasive healthcare
 application...
Outline

 Introduction
 RFID tags and healthcare
 Related literature on improving RFID tag identification
  accuracy
 ...
Introduction

 The primary goal of pervasive healthcare is to be able to
  deliver necessary quality healthcare service a...
Introduction

 RFID tags are used in scenarios
  an object needs to be identified
  tracked
  when ambient condition s...
RFID tags and healthcare

 One of the largest volumes of RFID application has been in
  the healthcare industry, where ab...
RFID tags and healthcare

 Three main areas benefit from RFID technology in the
 healthcare industry:
  (1) asset manage...
Related literature on improving RFID tag
          identification accuracy

 Bai et al. propose means to filter and clean ...
Proposed methods

 False positives and false negatives can be a problem in
  RFID-embedded systems, especially when signa...
Algorithm 1 (the base case)




                              10
Algorithm 2




              11
Algorithm 3




              12
Algorithm 4




              13
Algorithm 4




              14
Results

 We simulated the four algorithms presented above 10
  times with 1000 readings per run with the following
  ass...
Results




          16
The p-values for results
 (using pairwise t-test)




                           17
Average false positive cases using
different probability values for reader R1




                                        ...
Average false negative cases using
different probability values for reader R1




                                        ...
Average overall false cases using different
     probability values for reader R1




                                    ...
Average false positive cases using
different probability values for reader R2




                                        ...
Average false negative cases using
different probability values for reader R2




                                        ...
Average overall false cases using different
     probability values for reader R2




                                    ...
Discussion

 The algorithms to reduce false positives and false negatives
  while identifying the presence/absence of an ...
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090302 Identifying Rfid Embedded Objects In Pervasive Healthcare Applications

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090302 Identifying Rfid Embedded Objects In Pervasive Healthcare Applications

  1. 1. Identifying RFID- embedded objects in pervasive healthcare applications 1
  2. 2. Reference  Yu-Ju Tu, Wei Zhou, Selwyn Piramuthu “Identifying RFID-embedded objects in pervasive healthcare applications” , Decision Support Systems Volume 46 , Issue 2 (January 2009) 2
  3. 3. Outline  Introduction  RFID tags and healthcare  Related literature on improving RFID tag identification accuracy  Proposed methods  Discussion 3
  4. 4. Introduction  The primary goal of pervasive healthcare is to be able to deliver necessary quality healthcare service anytime to anyone regardless of location and other constraints.  The underlying principle in most of these intelligent information systems are rather similar to the extent that they all utilize knowledge in some form to enable decision making in the healthcare environment. Clinical Decision Support Systems (CDSS) Intelligent Decision Support Systems (IDSS) Healthcare Information Systems (HIS) 4
  5. 5. Introduction  RFID tags are used in scenarios an object needs to be identified tracked when ambient condition surrounding an object is captured stored  Although it is generally assumed that data read from RFID tags are highly accurate, variations in accuracy can and do occur due to several reasons. 5
  6. 6. RFID tags and healthcare  One of the largest volumes of RFID application has been in the healthcare industry, where about 4.5 million tags have been used every year on Diprivan drug syringes by AstraZeneca since 1999.  Asset tracking is a prime candidate for RFID applications A typical hospital is unable to locate about 15–20% of its assetswhen needed  Cost and privacy concerns have generally been recognized as major factors in the success of RFID applications 6
  7. 7. RFID tags and healthcare  Three main areas benefit from RFID technology in the healthcare industry: (1) asset management (2) patient care (3) inventory management 7
  8. 8. Related literature on improving RFID tag identification accuracy  Bai et al. propose means to filter and clean data streams from RFID applications that contain false (e.g., false positive, false negative) readings and duplicates  To improve RFID tag detection reliability, Agarwal et al. let the reader sample every 2 s  Data reliability and ambiguity are two major issues in extraction of information from RFID data  Khoussainova et al. use probabilistic method to provide the application with the flexibility to build its own balance between detection and precision rate 8
  9. 9. Proposed methods  False positives and false negatives can be a problem in RFID-embedded systems, especially when signal from a given tag is blocked by an impenetrable object (e.g., metal shielding) or when corrupted signal is read  Algorithm 1 (the base case)  Algorithm 2  Algorithm 3  Algorithm 4  Results 9
  10. 10. Algorithm 1 (the base case) 10
  11. 11. Algorithm 2 11
  12. 12. Algorithm 3 12
  13. 13. Algorithm 4 13
  14. 14. Algorithm 4 14
  15. 15. Results  We simulated the four algorithms presented above 10 times with 1000 readings per run with the following assumptions: The tag T (or, T1 in Algorithm 4) is always present (or, absent) during both the reads in a reader's field  Algorithm 1  Algorithm 2  Algorithm 3  Algorithm sliding window 15
  16. 16. Results 16
  17. 17. The p-values for results (using pairwise t-test) 17
  18. 18. Average false positive cases using different probability values for reader R1 18
  19. 19. Average false negative cases using different probability values for reader R1 19
  20. 20. Average overall false cases using different probability values for reader R1 20
  21. 21. Average false positive cases using different probability values for reader R2 21
  22. 22. Average false negative cases using different probability values for reader R2 22
  23. 23. Average overall false cases using different probability values for reader R2 23
  24. 24. Discussion  The algorithms to reduce false positives and false negatives while identifying the presence/absence of an RFID tag in the field of a reader, and illustrated these algorithms by means of an example scenario (Although our results are based on simplified assumptions)  Any successful attempt at improving true (positive and negative) readings would ultimately increase the performance and efficiency of RFID tag-enabled systems  This is even more salient given that the methods proposed in this paper can be implemented with minimal resources and effort 24

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