Phase difference based RFID navigation for medical applications

RFID localization is a promising new field of work that is eagerly awaited for many different types of applications. For use in a medical context, special requirements and limitations must be taken into account, especially regarding accuracy, reliability and operating range. In this paper we present an experimental setup for a medical navigation system based on RFID. For this we applied a machine learning algorithm, namely support vector regression, to phase difference data gathered from multiple RFID receivers. The performance was tested on six datasets of different shape and placement within the volume spanned by the receivers. In addition, two grid based training sets of different size were considered for the regression. Our results show that it is possible to reach an accuracy of tag localization that is sufficient for some medical applications. Although we could not reach an overall accuracy of less than one millimeter in our experiments so far, the deviation was limited to two millimeters in most cases and the general results indicate that application of RFID localization even to highly critical applications, e. g., for brain surgery, will be possible soon.

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