OnePlanet Research Center is combining multiple sensors and context data, to get a more fine-grained health status of a person over time which allows for more personal interventions. The platform that integrates data from sensors, prediction models and timely and personal interventions, we call the human digital twin.
A human digital twin based on the integration of new sensor- and AI technologies has the potential to have a disruptive impact on preventing disease in the healthy population and can at the same time accelerate research and innovation. Combining available data from different resources could strengthen our knowledge on preventive and health topics.
Technology for preventive health
In the current context of an aging population and increasing healthcare costs, preventive health becomes increasingly important. For example, diet is one of the major factors for prevention and management of chronic diseases, and crucial in the maintenance and improvement of overall health. However, one-size fits all diet advice is known to be ineffective. What works for you might not work for somebody else, because of genetics, microbiome, differences in behavior, preferences, etc. The keyword here is context. As an example, we know that stress is also affecting what, when and how much people eat, but is a risk factor for disease as well.
With a digital twin, sensor data is collected continuously and analysed using AI methods. With the twin we can learn over time what life style interventions works best for each individual person for different situations. These personalized interventions increase the health status of this person. This has the potential to result in an improvement of overall health, increased quality of life and a lifted burden on healthcare systems.
Early warning and personalized advice
The digital twin platform will be used to give insight in personal risk factors for, for example, cardiovascular disease, diabetes, metabolic disorders, burnouts, depression. It can also be used to give personalized advice in preventive health contexts like coaching programs or on GP’s advice when early health risk indicators are present. Underlying models and data can be used in research programs involving medical devices or food (supplement) / pharma platforms.
The full integration of data infrastructures, predictive models and intervention/recommendation tools is unique, especially within the combination of mental health and nutrition.
The development of the human digital twin is in its initial phase. A human digital twin based on the integration of new sensor- and AI technologies has the potential to have a disruptive impact on preventing disease in the healthy population and can at the same time accelerate research and innovation. Combining available data from different resources could strengthen our knowledge on preventive and health topics.