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Poster De Conférence Année : 2011

Global Strategy to Guaranty Dependability of Electrical Medical Implanted Devices

Résumé

Implanted devices for functional electrical stimulation are successfully used in a growing set of applications. Obviously, it is essential to guarantee the performance, efficiency and reliability of the systems during its entire life inside the human body. The devices have to be biocompatible, with an acceptable level of constraint for the user. Nowadays, advanced implanted devices have reached a high level of complexity, integration and heterogeneity, but reliability of the implants might remain questionable. In this paper, we propose a new dependability-oriented approach of the design of implanted systems. We have established the risk management procedure of the entire system in the form of an algorithm which has to be followed by everyone who participates to the design, the development and the use of the system. The main objective is to give tools and a methodology to maximize the dependability of the system. This algorithm consists of two kinds of concurrent risk analyses. The first risk analysis is dedicated to specific expertise fields or system parts and the second type of analysis use the data from these specific risk analyses to perform a global risk analysis at system level. For each risk analysis, we propose a concurrent top-down/bottom-up approach aiming to detect every critical part and providing guidelines to increase the dependability of both the electrical part of the implant and the whole system. Eventually, our objective is to use this electronic part of the implant as a monitor and a supervisor to increase the global dependability of the implant.
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Dates et versions

lirmm-00654942 , version 1 (24-12-2011)

Identifiants

Citer

Fanny Le Floch, Serge Bernard, Guilherme Bontorin, Fabien Soulier, Guy Cathébras. Global Strategy to Guaranty Dependability of Electrical Medical Implanted Devices. NER: Neural Engineering, Apr 2011, Cancun, Mexico. 5th IEEE EMBS International Conference on Neural Engineering, pp.515-518, 2011, ⟨10.1109/NER.2011.5910599⟩. ⟨lirmm-00654942⟩
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