Summary
EarlyCare project aims at developing and implementing cutting-edge sensing technologies and advanced Artificial Intelligence (AI) techniques to create a Health Monitoring System enabling early prediction of Diabetes Mellitus (DM) and Congestive Heart Failure (CHF), through the continuous monitoring of vital signs of older adults’ living alone related to micturition and cardiac activities as well as their warning signs. The innovative aspect of EarlyCare solution is the efficient deployment of completely nonwearable, unobtrusive, and privacy-friendly devices such as gas sensors and wireless physiological sensors which are coupled with explainable AI to detect and analyse the underestimated warning signs (i.e., nocturia and pollakiuria as abnormal micturition activity, arrhythmia and breathlessness). The AI-powered systems will be trained using publicly accessible sets and refined during the first phase of piloting. EarlyCare will be integrated into an interoperable IoT platform (enSenior) and validated on voluntary patients. The successful conclusion of this project will contribute to promote healthy ageing and reduce burden of formal and informal caregivers of older adults by advancing in development of eHealth tools and smart devices. The proposed multidisciplinary research activities, training, transferable skills and secondments will grant the improvement and acquisition of the most appropriate skills for his future career development.
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| Web resources: | https://cordis.europa.eu/project/id/101029808 |
| Start date: | 01-04-2021 |
| End date: | 18-09-2023 |
| Total budget - Public funding: | 172 932,48 Euro - 172 932,00 Euro |
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Original description
EarlyCare project aims at developing and implementing cutting-edge sensing technologies and advanced Artificial Intelligence (AI) techniques to create a Health Monitoring System enabling early prediction of Diabetes Mellitus (DM) and Congestive Heart Failure (CHF), through the continuous monitoring of vital signs of older adults’ living alone related to micturition and cardiac activities as well as their warning signs. The innovative aspect of EarlyCare solution is the efficient deployment of completely nonwearable, unobtrusive, and privacy-friendly devices such as gas sensors and wireless physiological sensors which are coupled with explainable AI to detect and analyse the underestimated warning signs (i.e., nocturia and pollakiuria as abnormal micturition activity, arrhythmia and breathlessness). The AI-powered systems will be trained using publicly accessible sets and refined during the first phase of piloting. EarlyCare will be integrated into an interoperable IoT platform (enSenior) and validated on voluntary patients. The successful conclusion of this project will contribute to promote healthy ageing and reduce burden of formal and informal caregivers of older adults by advancing in development of eHealth tools and smart devices. The proposed multidisciplinary research activities, training, transferable skills and secondments will grant the improvement and acquisition of the most appropriate skills for his future career development.Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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