Embedded Learning for Smart Functional Electrical Stimulation

Autor: Hervé Glotin, Edith Kussener, Sebastian Marzetti, Herve Barthelemy, Valentin Barchasz, Valentin Gies, Remy Vauche
Přispěvatelé: Institut des Matériaux, de Microélectronique et des Nanosciences de Provence (IM2NP), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique et Systèmes (LIS), Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 2020, Dubrovnik, Croatia. pp.1-6, ⟨10.1109/I2MTC43012.2020.9128681⟩
I2MTC
DOI: 10.1109/I2MTC43012.2020.9128681⟩
Popis: This paper presents a smart embedded Functional Electrical Stimulator (FES), able to stimulate a muscle only when a specific movement pattern occurs. This pattern is detected using an inertial measurement unit (IMU) coupled with a feature detector and a neural classifier. Architecture of the FES is first presented, then embedded processing algorithms composed of feature extraction and neural network classification are detailed. Results show that the muscle vibration happening when stimulation is needed can be recognized in more than 90% of cases using less than 3% of average embedded processor resources on a ARM M4F.
Databáze: OpenAIRE