Project description
Harnessing cardiac signals for tailored physical training
In diverse learning settings, individual capabilities and motivations vary, challenging the efficacy of standardised training approaches. Scientific literature underscores the potential of Cardiac Signals (CS) to gauge cognitive and physical states, crucial for tailored learning experiences. In this context, the ERC-funded SPEARS project will use CS to dynamically adapt to users’ cognitive and physical states. Building on the success of the BrainConquest project, which focused on personalised training via Brain-Computer Interfaces, SPEARS aims to enhance Machine Learning and Signal Processing algorithms for predicting performance using CS data from consumer-grade sensors like smartwatches. SPEARS aims to integrate its predictive technology into a sport training app, offering tailored training solutions for endurance athletes worldwide.
Objective
In any learning situation, be it math education, language learning or sport training, different learners have different abilities, motivations and capacities at any given time. Thus, an optimal learning can only be achieved with personalized training solutions, dynamically adapted to each learner’s cognitive and/or physical states. The scientific literature showed that such states could be estimated from Cardiac Signals (CS). In ERC PoC SPEARS, we thus propose to redefine consumer training apps, by enabling them to propose personalized and adaptive training plans according to an estimation of their users’ cognitive and/or physical states from their CS measured with consumer grade sensors, e.g. smartwatches. The outcome of ERC project BrainConquest should enable us to tackle this challenge. Indeed, in BrainConquest we explored such a personalized training approach for users of Brain-Computer Interfaces (BCI). In doing so, we developed Machine Learning (ML) and Signal Processing (SP) algorithms to estimate users’ mental states and predict their upcoming performances from their brain and physiological signals, including CS. In SPEARS, we thus aim at adapting, improving and assessing BrainConquest ML & SP algorithms, initially designed for BCI performance prediction from research grade brain and CS sensors in the lab, to predict cognitive and physical performance from consumer grade CS sensors in the wild. Such algorithms could be used for adaptive training apps in education, cognitive training for healthy aging or sport training. We will then explore a commercial application of this technology for sport training in particular, in collaboration with the startup Flit Sport, which sells an app for providing personalized training exercises for endurance sport athletes, based on their past performances and ML. By integrating our CS-based prediction into Flit Sport training app, we should design optimally personalized training solutions for millions of runners worldwide.
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.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
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.
-
HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
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
See all projects funded under this funding scheme
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-2023-POC
See all projects funded under this callHost institution
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.
78153 Le Chesnay Cedex
France
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.