Objective
Clinical evidence has shown that functional electrical stimulation (FES) improves hand movements in patients with motor disabilities, helping stroke or spinal cord injury patients regain limb function. Multi-array FES uses a high-density electrode array that can selectively activate specific muscle groups and enable selective hand movements. However, user-specific neuromuscular behavior and unknown electrode positions require calibration at each use. This process is time-consuming, error-prone, and dependent on clinical expertise, making manual calibration a major bottleneck to clinical and commercial adoption. Automating calibration reduces setup time, leaving more time for therapy and enabling unsupervised use in rehabilitation and activities of daily living (ADL). We developed and validated a robust Bayesian optimisation for reliable automatic FES calibration, showing strong performance in healthy participants. However, translation to clinical populations requires patient data because pathological muscle physiology (nonlinear neuromuscular dynamics, altered muscle tone and spasticity, and abnormal muscle synergy) introduces patient-specific behaviors that must be explicitly modelled and cannot be reliably inferred from healthy participants. To address this gap and increase TRL, this project collects and analyses clinical data from post-stroke patients and defines clinical constraints, performance requirements, and usability specifications for high-TRL system validation. Clinical data allows identification of patient-specific neuromuscular characteristics needed for individualised modeling and the derivation of performance metrics assessing calibration efficiency, stimulation selectivity, functional task performance, and system usability and robustness. These evaluations aim to establish automatic multi-array FES calibration (Auto-FES) as a practical, robust, easy-to-operate assistive technology suitable for unsupervised use in clinical and home settings.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2026-POC
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
80333 Muenchen
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.