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.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
78153 Le Chesnay Cedex
France