Int J Med Robot - Correction method for wireless electromagnetic localization of microcapsule devices.

Tópicos

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Resumo

CKGROUND: In the development of a telemetry localization system for wireless tracking of a microcapsule medical device based on alternating current magnetic fields, further improvements are required in terms of localization accuracy and reductions in systematic error.METHODS: A new correction method is proposed based on an improved neural network algorithm for wireless localization. Based on the wireless localization model and its prototype, a single neural network with five input and five output neurons was designed for correction. Because the position and attitude angle are defined on different domains, both the input and output variables were normalized to improve network convergence. To prevent overfitting, the Levenberg-Marquardt Bayesian regularization algorithm was used as an effective learning algorithm for the neural network.RESULTS: Through experimental testing, the tracked and true locations were obtained, and the effects of neural network correction on improving localization accuracy were assessed. The experiments demonstrated reductions in localization errors when using the improved neural network correction algorithm. After correction, average errors of the X, Y, Z, a, and ? components reduced to 8.1mm, 9.3mm, 7.2mm, 0.075rad, and 0.071rad, respectively.CONCLUSIONS: Compared with the basic back propagation algorithm, the Levenberg-Marquardt Bayesian regularization algorithm effectively improves the generalizability and convergence accuracy of neural networks in wireless localization correction. In addition, this method provides a feasible solution for improving the accuracy when wirelessly tracking a microcapsule device.

Resumo Limpo

ckground develop telemetri local system wireless track microcapsul medic devic base altern current magnet field improv requir term local accuraci reduct systemat errormethod new correct method propos base improv neural network algorithm wireless local base wireless local model prototyp singl neural network five input five output neuron design correct posit attitud angl defin differ domain input output variabl normal improv network converg prevent overfit levenbergmarquardt bayesian regular algorithm use effect learn algorithm neural networkresult experiment test track true locat obtain effect neural network correct improv local accuraci assess experi demonstr reduct local error use improv neural network correct algorithm correct averag error x y z compon reduc mm mm mm rad rad respectivelyconclus compar basic back propag algorithm levenbergmarquardt bayesian regular algorithm effect improv generaliz converg accuraci neural network wireless local correct addit method provid feasibl solut improv accuraci wireless track microcapsul devic

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