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RFID-based surgical instrument detection using Hidden Markov models

Authors: C. Meißner; T. Neumuth;

RFID-based surgical instrument detection using Hidden Markov models

Abstract

Abstract This paper is about instrument localization in the operating room during surgeries. The main purpose of the proposed system is the automatic determination of the possible instrument usage information for automatic surgical workflow re-cording. The approach is based on Radio Frequency Identification (RFID), sensor fusion methods and Hidden Markov models (HMM). The Results show high accuracy for instrument localization and demonstrate the ability of the system to replace manually recorded instrument usage information. 1 Introduction Since the number of medical devices in the operating room is increasing and the handling of them is getting more complex, there is an upcoming demand for automatic as-sistance systems. Major requirement for timely assistance is to recognize the current surgical activity of the interven-tion. The recognition of the current activity enables the sit-uation dependent assistance of the surgeon. Previous works dealt with the manual recording of workflow activi-ties [4]. The goal of current research is to automate the process of workflow recording. A

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Average
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