This page gives an overview of research projects I am or have been involved in.
selfBACK: A decision support system for self-management of low back pain
Project summary: The recent global burden of disease study showed that low back pain (LBP) is the most significant contributor to disability in Europe. Most patients seen in primary care with LBP have non-specific LBP (≥85%), i.e., pain that cannot reliably be attributed to a specific disease/pathology. LBP is the fourth most common diagnosis seen in primary care (after upper respiratory infection, hypertension, and coughing). Self-management in the form of physical activity and strength/stretching exercises constitutes the core component in the management of non-specific LBP; however, adherence to self-management challenging due to lack of feedback and reinforcement. This project aims to develop a decision support system – selfBACK – that will be used by the patient him/herself to facilitate, improve and reinforce self-management of LBP. Specifically, selfBACK will be designed to assist the patient in deciding and reinforcing the appropriate actions to manage own LBP after consulting a health care professional in primary care. The decision support will be conveyed to the patient via a smartphone app in the form of advice for self-management. The advice will be tailored to each patient based on the symptom state, symptom progression, the patients goal-setting, and a range of patient characteristics including information from a physical activity-detecting wristband worn by the patient. The second part of the project will evaluate the effectiveness of selfBACK in a randomized controlled trial using pain-related disability as primary outcome. We envisage that patients who use selfBACK will have 20% reduction in pain-related disability at 9 months follow-up compared to patients receiving treatment as usual. Process evaluation will be carried out as an integrated part of the trial to document the implementation and map the patients’ satisfaction with selfBACK. A business plan with a targeted commercialisation strategy will be developed to transfer the selfBACK technology into the market.
Role: I served as project manager for the overall project which is coordinated at NTNU. Further I have been responsible for the overall technical development in the project.
Funding: 3,92 M EUR through EU H2020 RIA. Grant agreement ID: 689043.
Duration: 1. Jan 2016 – 31. Dec 2020
AI4EU: European Artificial Intelligence On-Demand Platform and Ecosystem
Project summary: Artificial Intelligence is a disruptive technology of our times with expected impacts rivalling those of electricity or printing. Resources for innovation are currently dominated by giant tech companies in North America and China. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources. AI4EU will efficiently build a comprehensive European AI-on-demand platform to lower barriers to innovation, to boost technology transfer and catalyse the growth of start-ups and SMEs in all sectors through Open calls and other actions. The platform will act as a broker, developer and one-stop shop providing and showcasing services, expertise, algorithms, software frameworks, development tools, components, modules, data, computing resources, prototyping functions and access to funding. Training will enable different user communities (engineers, civic leaders, etc.) to obtain skills and certifications. The AI4EU Platform will establish a world reference, built upon and interoperable with existing AI and data components (e.g. the Acumos open-source framework, QWT search engine..) and platforms. It will mobilize the whole European AI ecosystem and already unites 80 partners in 21 countries including researchers, innovators and related talents. Eight industry-driven AI pilots will demonstrate the value of the platform as an innovation tool. In order to enhance the platform, research on five key interconnected AI scientific areas will be carried out using platform technologies and results will be implemented. The pilots and research will showcase how AI4EU can stimulate scientific discovery and technological innovation. The AI4EU Ethical Observatory will be established to ensure the respect of human centred AI values and European regulations. Sustainability will be ensured via the creation of the AI4EU Foundation. The results will feed a new and comprehensive Strategic Research Innovation Agenda for Europe.
Role: Participation in Task 6.8 – AI4IoT pilot. This pilot explores the use of the AI4EU platform on air quality data captured by IoT devices. This includes deploying micro sensors, creating a data management platform for storing and processing data as well as running experiments targeting air quality prediction and decision support through machine learning models, simulations and visualisations. To improve data quality and services the pilot enhances pollution data with other information such as mobility patterns, weather forecasts, and environmental data.
Funding: 20,7 M EUR through EU H2020 RIA. Grant agreement ID: 825619.
Duration: 1. Jan 2019 – 31. Dec 2021
Back-UP: evidence-based management of neck and low back pain through a technological platform with prognostic models.
SupportPRIM: Decision support system for management of musculoskeletal disorders.
PA & Sleep: Data in the HUNT study (Nord-Trøndelag Health Study) to examine how biological, socioeconomic, individual, and demographic determinants affect physical behaviour and sleep.
Exposed SFI: Knowledge and technology for robust, safe and efficient fish farming at exposed locations.