EASO is a partner in the H2020 PROTEIN project. Launched today, this research project is led by a consortium of European public and private sector organisations working to promote health and well-being. Using novel communication and machine learning technologies, PROTEIN will develop tools to personalise support for nutrition and physical activity for EU citizens.
A new project, led by the Centre for Research and Technology Hellas (CERTH) will provide personalized nutrition and physical activity support to consumers.
PROTEIN (PeRsOnalized nutriTion for hEalthy livINg), a three-and-a-half year Euro-pean Commission Horizon 2020 funded initiative, is supported by a consortium of 20 pan-European technology, research, consumer and industry partners.
This research initiative will develop and supply personalized nutrition and physical ac-tivity support using Artificial Intelligence (AI) information and techniques so that people can make healthier choices based on their unique needs, behaviours and preferences.
Research has shown that that nutrition and physical activity patterns are important in preventing and managing chronic diseases such as diabetes, heart disease and obe-sity. Developing tools and resources to help individuals develop and maintain healthy eating and physical activity patterns can be a path to reducing the burden of chronic diseases at the population level. However, since healthy eating and physical activity habit patterns depend on many individual, social and cultural factors, tools and resources need to be personalized and contextualized to meet consumer needs.
Researchers will assess what works best for particular people, and PROTEIN will de-liver personalized support and resources to individual consumers making use of pio-neering technologies which enable personalised recommendations.
“New advances in information technologies, particularly in wearable sensors, big data analysis and machine learning techniques, as well as in direct-to-consumer genetic testing, blood and gut microbiome analysis, open up new opportunities for research-ers to monitor and collect information related to our dietary behaviours,” said PRO-TEIN project lead Lazaros Gymnopoulos, CERTH.
PROTEIN partners will engage consumers through grocery stores, online supermar-kets, restaurants, and digital shops to develop a platform of tools and resources which can personalize dietary programs and physical activity plans. The project aims to provide a suite of tools individuals can use to personalize healthier grocery pur-chases and physical activity habits based on their own needs and preferences.
“The PROTEIN project aims to support personalised choices when shopping for nutri-tious and enjoyable food, which contributes to a healthy lifestyle when combined with activity guidance. The research, development, and trials in the project will help us evaluate how the mobile app uses sensors and AI to inform the individual about appropriate selections that make online grocery shopping part of managing health for you and your family.” (Duncan Russell, Ocado Technology)
The PROTEIN team is a unique and interdisciplinary group of people and organiza-tions who want nothing less than to revolutionize the way citizens receive personal-ized healthy living advice. Experts from engineering and health sciences, including machine learning experts, dietitians, medical clinicians, physical activity profession-als, biomedical experts, and social scientists will work together to implement this in-novative and enterprising research program. It is an ambitious project that aims to change the way we engage in healthy eating and physical activity across communi-ties and within populations. Whether you are a person living with a chronic disease, an elite athlete or an ordinary citizen interested in improving your overall health and well-being, PROTEIN tools and resources could help you achieve your personal goals.
PROTEIN website:
https://protein-h2020.eu
Twitter:
@ProteinEU
For more information contact
Lazaros Gymnopoulos, CERTH info@proteinproject.eu
This project has received funding from the European Union’s Horizon 2020 research and in-novation programme under grant agreement No 817732