An Anti-Collision Scheme for RFID for Patient Tracking Using Linear Interpolation Estimation.

Radio Frequency Identification (RFID) tags are widely used in the healthcare industry for patient tracking. A mainstream RFID implementation is based on a series of readers installed in a fixed location within a hospital or a nursing home and tags are embedded in the clothing worn by patients. Careg...

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Publicado en:Journal of Medical Systems Vol. 44; no. 10
Autor principal: Fong, Bernard
Formato: algorithm equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Oct2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2020
      vid: 44
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-020-01647-x
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        atl: An Anti-Collision Scheme for RFID for Patient Tracking Using Linear Interpolation Estimation.
      aug:
        au: Fong, Bernard
        affil: Providence University, Taiwan Blvd Sec 7 No 200, 433, Taichung City, Taiwan
      sug:
        subj:
          Radio Frequency Identification
          Patient Identification
          Algorithms Utilization
          Emergency Medical Tags
          Human
          Health Facility Environment
          Probability
          Descriptive Statistics
          Computer Simulation
          Wearable Sensors
      ab: Radio Frequency Identification (RFID) tags are widely used in the healthcare industry for patient tracking. A mainstream RFID implementation is based on a series of readers installed in a fixed location within a hospital or a nursing home and tags are embedded in the clothing worn by patients. Caregivers can readily obtain near real-time location information of individual patients from the tag locations. For implementation in washable clothing tags are often passive such that tag collision is a common problem within co-operation mechanism between tags. Tag anti-collision scheme is there an important consideration that affects the identification effectiveness. To address this issue, this paper proposes a dynamic frame slotted Aloha algorithm based on linear interpolation based estimation that adaptively adjusts the frame length. Simulation results show that the proposed algorithm yields an estimation error below 1.5% achieved in less than 10 iterations, it provides reduction in identification time while reduces the tags leakage probability in a clinical environment where patient tracking is automatically managed.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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