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Neuroprosthesis-UI

Financiado

Cerrado

Neuroprosthesis user interface based on residual motor skills and muscle activit...

Neuroprosthesis user interface based on residual motor skills and muscle activity in persons with upper limb disabilities In this project, I will develop a user interface that will allow persons with upper limb disabilities to control neuroprosthesis using their residual motor skills. This interface will consist of inertial sensors (IMU) and electrom... ver más

30/06/2022

INRIA

197K€

Presupuesto del proyecto: 197K€

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-06-30

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

INRIA

196.71K€ | Lider

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Duración del proyecto: 26 meses Fecha Inicio: 2020-04-21
Fecha Fin: 2022-06-30

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 197K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In this project, I will develop a user interface that will allow persons with upper limb disabilities to control neuroprosthesis using their residual motor skills. This interface will consist of inertial sensors (IMU) and electromyography (EMG) that are capable of capturing movements and muscle contraction that even persons with high tetraplegia still can control. The interface will also be able to learn different inputs, customizing the system for each user. This requires techniques of machine learning, making it flexible and indicated for users with different upper limb disabilities, such as spinal cord injury, stroke and multiple sclerosis. The machine learning techniques will classify the user inputs into desired commands, working as an intention decoder. The interface will be used to control a hybrid upper limb neuroprosthesis based on surface functional electrical stimulation (FES) and a semi passive mechanical orthosis. The system will allow users to perform activities of daily life independently. To my knowledge, such a hybrid system with FES, and controlled by an interface based on IMUs, EMG and machine learning techniques is novel. I will be working with Christine Coste, an expert in neuroprosthesis for disabled persons, and her interdisciplinary team, which consist of engineers and health professionals with vast experience in neurorehabilitation. This fellowship will enable the transfer of knowledge between her team and me through experiments with real patients and mutual training. I can contribute to the team with my expertise in machine learning and control, whereas they have vast access to patients, medical doctors, mechanical designers, electrical stimulators and sensors. This project is going to be an important step in my career as expand my network in Europe, develop my skills as a biomedical engineer and improve my research experience towards becoming a world-leading expert in neurorehabilitation engineering.

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