DEVELOPING A SAFE PERSONAL HEALTH COACHING PROGRAMME

ONDERZOEK IN DE KIJKER

Current healthcare systems are under pressure due to a sharp increase in chronic diseases. Personal coaching programmes can prevent health problems by guiding people towards a healthier lifestyle. As part of the European HEART-project, a team of international researchers developed a system that is able to detect human activities from heterogeneous data while safeguarding the privacy. Chetanya Puri and Hee Reen Shim from the e-Media Research Lab (Group T Campus) and Koustabh Dolui from the imec DistriNet Research Group tell the story.

HEART stands for ‘HEalth-related Activity Recognition system based on Internet of Things’. It is one of the very first industrial doctorates founded under the Marie Sklodowska-Curie Actions, integrating information technology with social sciences and humanities. In the HEART-project, not only the technological innovative power of IoT is investigated, but also the needs of the customer or user, including legal issues. The Activity Recognition System is expected to be applied in the wearable sensor technology business in order to personalise health-coaching programmes. Moreover, HEART also defined a penetration strategy for the Chinese market that ensures both protection of personal information and adaptation to the needs of the Chinse customers.

Chetanya Puri and Koustabh Dolui

©Julie Feyaerts

The Heart project covers six individual research projects, closely interrelated, concluded by six excellent young researchers: four PhD students in ICT, one in legal studies and one in international business. Research was mainly conducted at KU Leuven and the University of Macerata (Italy) and at Philips, the leading multinational in the healthcare sector, with the support of a network of European and Chinese partners, such as Fudan University (Shanghai) and the University of the Chinese Academy of Sciences (UCAS).

Early stage researchers

Chetanya Puri enrolled in the HEART-project as one of the six early stage researchers. He received a Master’s degree in Telecommunication Systems Engineering from the Indian Institute of Technology in Kharagpur (India). Then he joined the industry, where he was involved in building anomaly detection techniques for cardiac health estimation, using signals from wearables and other sensors linked to smartphones.

Koustabh Dolui also has a background in engineering. He obtained his Master in Telecommunications Engineering at Politecnico di Milano (Italy) and has experience as research engineer in a European project. He was part of the development team for the EU Horizon 2020 project AGILE, working on data collection, cloud integration and device management on the AGILE gateway. Koustabh is researcher with the Department of Computer Science and imec DistriNet.

Hee Reen Shim obtained her Master’s degree in Electrical and Electronics Engineering from Chung-Ang University in Seoul (Korea). She worked as a researcher at the Artificial Intelligence Lab at Korea Institute of Industrial Technology, where she focused on developing machine-learning algorithms and designing deep learning architecture. Currently, Hee Reen is a member of the e-Media Research Group at Group T Leuven Campus.

Heterogeneous data

Hee Reen’s PhD project consists of developing a health activity recognition system from heterogeneous data. “I worked on analysing the user’s feedback in language in order to verify his or her health condition. As sensors can only capture biophysical parameters, it is quite difficult to understand what the user exactly means, when he says he is experiencing a health problem. That is why I built a neural network to understand the people’s problems, based on their oral or written declarations. As I did a lot of my research at the Personal Health Department of Philips Research Europe, I had the advantage of investigating potential industrial applications”.

Koustabh focused on IoT cloud platforms and middleware, privacy and aggregation for IoT and Edge computing. “I took the data and processing applications used by Hee Reen and Chetanya and looked for ways of transferring them from a traditional cloud platform to mobile devices, IoT gateways and even microcontrollers”.

Labelling

Chetanya concentrated on building machine learning algorithms that learn and classify daily activities from wearable and IoT devices. “This raises the problem of data labelling. Not everyone experiences a health problem in the same way and names it differently. On the other hand, the industry is asking for solutions that are generally applicable to the entire population. My work consisted of developing an algorithm that is able to learn from limited labelled data and predict the condition as early as possible. Currently, I am working on weight gain data from pregnant women. The aim is to develop models that detect whether or not there is a risk of permanent obesity after pregnancy. This data is then passed on to doctors or health care providers, who can make appropriate recommendations”.

Final conference

The final conference of the project took place on 6 July 2021, where both the scientific findings and the training results were presented. The final event also provided an opportunity to highlight the innovative doctoral training with emphasis on interdisciplinarity, transversal skills, the international dimension and cooperation with industry.

Yves Persoons

www.heart-itn.eu