Accelerate innovation in medical devices by “Moore for Medical”
01/06/2020 – 31/05/2023
€66M total, €1.7M University of Turku
Moore4Medical will accelerate innovation in electronic medical devices. The project addresses emerging applications and technologies that offer exciting new opportunities for the Electronic Components and Systems (ECS) industry. Moore4Medical will focus on the development of open and enabling technology platforms for these emerging fields to help them bridge “the Valley of Death” in shorter time and at lower cost. With value and IP moving from the technology level towards applications and solutions, defragmentation and open technology platforms will be key in acquiring and maintaining European leadership in the forefront of affordable healthcare.
In Moore4Medical, a new cost-effective and user-friendly platform for long-term at-home monitoring and follow-up of users will be developed. The platform will feature artificial
intelligence and machine-learning-based algorithms. The developed solutions will be clinically tested and validated during the Moore4Medical project, and be thus ready for commercial
exploitation in late 2023. Moore4Medical approaches Remote Cardiac Monitoring (RCM) via two complementary approaches: (a) measuring the motion of the bed upon which the person is resting and (b) measuring chest motion via remote sensing.
Privacy-preserving AI for Synthetic and Anonymous Health Data
01/02/2020 – 31/08/2020 (Co-creation)
01/01/2021 – 31/12/2023 (Co-innovation)
100 000 € (Co-creation)
PRIVASA boosts data-driven research, development and innovation by making it easier to access and share health data without compromising individuals’ privacy. The core aim of the project is to add flexibility in the way that health data can be utilized in various stages of product development. This aim extends beyond technical solutions, as it also requires trust-building between owners, holders and users of the data. Transparency, legal aspects and ethics are the cross-cutting themes to be explored throughout the project.
PRIVASA develops tools that apply latest AI methods to support the growth of health technology businesses. Privacy-preserving algorithms convert sensitive data into a safe but useful anonymous format that is suitable for medical research, testing and validation. The use of anonymous data simplifies and speeds up the development cycles. The project addresses the special challenges related to medical images and free form patient records. PRIVASA offers explainable AI solutions that can be implemented as database interfaces or stand-alone services.
Monitoring solution for accessing lung and heart diseases
1.9.2020 – 31.8.2022
799 000 euros
The commercialization of a solution related to a heart failure (HF) and chronic obstructive pulmonary disease (COPD) is investigated in this project. This project addresses the problem that currently many of these patients are not able to recognize when their condition gets weaker. A solution enabling patients to monitor the progression of their disease and detect when the disease gets worse in time, thereby avoiding the acute hospital readmissions is investigated. The aim is to develop a proof-of-concept solution during the project.
Site of Research: Department of Future Technologies, University of Turku
Advanced packaging for photonics, optics and electronics for low cost manufacturing in Europe
36 months, 2019-2022
€34M total, €1M University of Turku
A consortium of 31 European electronics packaging, optics and photonics key players, leading equipment suppliers and testing experts launched a new project, “Advanced packaging for photonics, optics and electronics for low cost manufacturing in Europe,” simply called APPLAUSE. The project fosters the European semiconductor value chain by building new tools, methods and processes for high volume manufacturing. The €34M total budget for the three-year project (2019-2022) is co-funded by Horizon 2020 and national funding agencies and industries, as a part of the Electronics Components and Systems for European Leadership Joint Undertaking (ECSEL JU). In Finland, national public funding is provided by Business Finland.
The six use cases of APPLAUSE are: (i) a substantially smaller 3D integrated ambient light sensor for mobile and wearable applications; (ii) a high performance, low cost, uncooled thermal IR sensor for automotive and surveillance applications; (iii) high speed datacom transceivers with reduced manufacturing costs; (iv) a flexible cardiac monitoring patch; (v) miniaturized cardiac implants with advanced monitoring capabilities; and (vi) an optical water measurement module with cost-effective packaging of components. With a budget of €1M, the UTU Health Technology Group participates in the flexible cardiac monitoring patch use case, where it is responsible for gathering and analyzing patient data and creating new advanced algorithms for detection of cardiac anomalies – atrial fibrillation, heart failure and coronary artery disease.
Project website: https://applause-ecsel.eu
Supporting Lifestyle Change in Obese Pregnant Mothers through Wearable Internet-of-Things
2018 – 2022
600 000 euros
Overweight (BMI > 25) and obesity (BMI > 30) are constantly growing problems causing many health consequences. Correspondingly, the prevalence of overweight and obesity in pregnant women is increasing. Pregnant women with obesity have indisputably increased risk for gestational diabetes mellitus, depression, and miscarriage, just to mention few. These pregnancy complications have negative effects on their unborn children. Physiological parameters and the information on pregnant women’s lifestyle and behavioral patterns –e.g., communication, eating, and recreation– can be used to investigate multifactorial processes that maintain obesity and leads to negative pregnancy outcomes. Moreover, monitoring of such parameters and patterns would help healthcare professionals and women themselves to monitor and tailor personalized interventions to minimize weight gain and adopt a healthier lifestyle during pregnancy. We believe the Internet of Things (IoT) technologies can be exploited to bring intelligence and ubiquitous monitoring to maternity care. In this project, an Internet-of-Things-based intelligent monitoring system is developed to detect and predict obesity-related pregnancy complications as early as possible. The cybernetic health concept is utilized by intertwining lifestyle and environmental data together with bio-signals associated with medical knowledge to develop a closed-loop system to make maternity care more effective, dynamic, and end-user driven. This is done via a platform that leverages portable devices and inexpensive wearable sensors, coupled with a multimodal event modeling, activity recognition, and life-logging engine. This research delivers a ubiquitous pregnancy monitoring service to end-users, mothers, and healthcare providers, enabling pregnancy events detection, prediction, assessment, and prevention.
Site of Research:
Department of Future Technologies, University of Turku (FT-UTU)
Department of Nursing Science, University of Turku (NS-UTU)
Turku University Hospital(TYKS)
Acute Heart Attack Detection (akuutin sydänkohtauksen detektointi)
1.8.2019 – 31.1.2021
700 000 euros
Approximate Computing for Power and Energy Optimisation
1.11.2020 – 31.10.2024
300 000 euros
15 Early Stage Researchers to tackle the challenges of future embedded and high-performance computing energy efficiency by using disruptive methodologies. Following the current trend, by 2040 computers will need more electricity than the world energy resources can generate. On the communications side, energy consumption in mobile broadband networks is comparable to datacenters. To make things worse, Internet-of-Things will soon connect 20 to 50 billion devices through wireless networks to the cloud. APROPOS aims at decreasing energy consumption in both distributed computing and communications for cloud-based cyber-physical systems. We propose adaptive Approximate Computing to optimize energy-accuracy trade-offs. Luckily, in many parts of the global data acquisition, transfer, computation, and storage systems there exists the possibility to trade off accuracy to either less power or less time consumed – or both. As examples, numerous sensors are measuring noisy or inexact inputs; the algorithms processing the acquired signals can be stochastic; the applications using the data may be satisfied with an “acceptable” accuracy instead of exact and absolutely correct results; the system may be resilient against occasional errors; and a coarse classification or finding the most probable matches may be enough for a data mining system. By introducing a new dimension, accuracy, to the design optimization, the energy efficiency can even be improved by a factor of 10x-50x. We will train the spearheads of the future generation to cope with the technologies, methodologies, and tools for successfully applying Approximate Computing to power and energy saving. The training, in this first ever ITN addressing approximate computing, is to a large extent done by researching energy-accuracy trade-offs on circuit, architecture, software, and system-level solutions, bringing together world leading experts from European organizations. In addition, we will provide network-wide and local trainings on the substance and on the complementary skills needed in both industrial and academic work life.
Wearable blood pressure monitoring
1.11.2018 – 1.6.2021
Cardiovascular diseases are the most common cause of death in the world accounting for approximately 30% of all deaths worldwide. Fortunately, many of these deaths could be prevented by early treatment and lifestyle choices. Elevated blood pressure, is both a symptom and a cause for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Approximately 2 million people suffer from hypertension in Finland and only half of them receive proper medication. Advancements in wearables and precision health solutions are currently transforming the health landscape and it is increasingly more possible to track changes in personal health conditions more accurately. The goal of this research is to develop and validate a new non-invasive medical instrumentation technologies for assessing hemodynamic status using new sensing principles and algorithmic techniques. We believe that new hemodynamic monitoring solutions for long term continuous monitoring, which has not been feasible before due to absence of suitable instruments, new improved precision health monitoring solutions can be achieved.
MEMS-based Intrafraction Motion Tracking for PET/CT and Radiotherapy
1.1.2018 – 31.12.2021
500 000 euros
In this project we develop new methods for motion compensation and gating in PET/CT imaging and radiotherapy. Our approach is based on measuring motions from multiple locations in the upper body using several highly sensitive inertial measurement units, and on fusing this information with that obtained from the PET/CT scanners.
With more accurate motion measurements available for PET/CT imaging, multiple gains can be achieved. Improving the quality of PET/CT images in both cardiology and oncology is beneficial for standard uptake value quantification and image quality, resulting to more accurate diagnosis and treatment decisions.
Approximate Computing for Smart Edge Processing
1.9.2017 – 31.8.2021
590 000 euros
Emerging application domains such as artificial intelligence, virtual and augmented reality, cyber-physical systems and Internet-of-things (IoT) etc., provide intelligence to machines and the environment in which they function by integrating sensing, computation and actuation capabilities. To make intelligent decisions and influence the environment meaningfully, these applications require high performance in real-time, beyond the scope of traditional computing applications. Ambient intelligence application domains, in particular in the context of Internet-of-Things, require high performance in real-time within lower energy budgets. Power challenges have limited the performance and energy gains of existing mobile processing platforms. Approximate Computing has emerged as an alternative that leverages inherent error resilience in computing applications to trade-off accuracy for performance and energy gains. We address the performance and energy issues of emerging applications and power challenges of existing hardware platforms simultaneously through approximate computing. The objective of this proposal is to implement hardware and software techniques to design an ecosystem that supports approximation. We orchestrate approximation in two steps, software layer – to identify and annotate approximable regions of applications, hardware layer- to exploit error resilient tasks by executing them on inaccurate hardware. We design modular reconfigurable processor cores, butterfly cores, that can switch their mode of execution from accurate to approximate on demand, based on application requirements and system dynamics. We integrate butterfly cores into a chip multi-processor, the butterfly processor, for high performance and flexibility in choice of quality of service. We bridge the gap among different layers of computing stack in realizing approximate hardware through architectural design and implementation of a general purpose approximate hardware. To support the run-time reconfiguration of processor mode (accurate and approximate), we implement hierarchical system management to optimize power, energy, performance and accuracy at the same time.