Project description
A novel neuroprosthesis control system
Neuroprosthetic devices employ electrodes to interface with the nervous system and attempt to restore loss of function or movement. Scientists of the EU-funded Neuroprosthesis-UI project propose to develop a user interface that will allow people with upper limb disabilities to control the neuroprosthesis using residual motor skills. This interface will comprise different sensors to capture muscle contraction, and with the help of machine learning, it will decode user input into movement intention. This hybrid system will assist patients with upper limb disabilities such as spinal cord injury, stroke and multiple sclerosis in performing activities of daily life independently.
Objective
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.
Fields of science
- medical and health sciencesbasic medicineneurologymultiple sclerosis
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- medical and health sciencesbasic medicineneurologystroke
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
78153 Le Chesnay Cedex
France