Summary
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/899040 |
| Start date: | 01-07-2020 |
| End date: | 30-06-2022 |
| Total budget - Public funding: | 196 707,84 Euro - 196 707,00 Euro |
Cordis data
Original description
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.Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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