SciTransfer
STOP · Project

Smart Sensor and Chatbot Platform Helping People Beat Obesity

healthTestedTRL 5

Imagine a personal nutrition coach that lives on your phone, watches data from your wearable sensors, and chats with you about what you eat. Researchers from 7 organizations across 4 countries built exactly that — an AI platform that collects health data, analyzes eating patterns with machine learning, and uses game-like challenges to nudge people toward healthier food choices. Doctors can monitor everything remotely. There is even a "digital mirror" feature that shows you how today's food choices could affect your future health and appearance.

By the numbers
7
consortium partners collaborating on the platform
4
countries represented (DE, IE, IT, UK)
3
SMEs involved in development
43%
industry partner ratio in the consortium
6
software deliverables released (two full release cycles)
EUR 929,200
EU funding received
The business problem

What needed solving

Obesity costs European healthcare systems billions annually, driving conditions like diabetes, heart disease, and liver problems. Current nutrition apps rely on manual food logging and generic advice, leading to low engagement and poor long-term results. Healthcare professionals lack real-time visibility into what patients actually eat between appointments, making chronic obesity management reactive rather than preventive.

The solution

What was built

The team built a complete digital health platform released in two full cycles, consisting of three layers: a Data and Knowledge Management layer for collecting and processing sensor and chatbot data, a Big Data Application layer for ML-driven analysis, and the integrated STOP Platform itself. In total, 6 software releases and 11 deliverables were produced over the 4-year project.

Audience

Who needs this

Health insurance companies looking to reduce obesity-related claim costsDigital health startups building nutrition coaching or chronic disease management appsHospital networks and obesity clinics needing remote patient monitoring toolsCorporate wellness providers seeking engaging employee health platformsMedical device companies wanting to integrate AI nutrition analysis with their sensor hardware
Business applications

Who can put this to work

Health Insurance
enterprise
Target: Health insurance providers managing obesity-related claims

If you are a health insurer dealing with rising costs from obesity-related conditions like diabetes, heart disease, and liver problems — this project developed an AI platform combining wearable sensors and chatbot coaching that helps policyholders improve nutrition habits. With 7 partners across 4 countries validating the approach, the gamification-driven system could reduce long-term treatment costs by keeping members healthier.

Corporate Wellness
mid-size
Target: Corporate wellness and employee health platform providers

If you are a wellness platform provider struggling with low employee engagement in health programs — this project built a gamified nutrition coaching system powered by smart sensors and AI chatbots. The platform went through two full release cycles with 3 industry partners involved in development, meaning the technology is ready for integration into existing corporate health offerings.

Hospital & Clinical Care
enterprise
Target: Hospital networks and obesity clinics running patient management programs

If you are a hospital or clinic managing chronic obesity patients and need better remote monitoring tools — this project created a platform where healthcare professionals supervise AI-analyzed sensor data and chatbot interactions with patients. With 6 software deliverables including a complete platform release, this gives clinicians real-time visibility into patient nutrition behavior between appointments.

Frequently asked

Quick answers

What would it cost to license or deploy this platform?

The project received EUR 929,200 in EU funding under the MSCA-RISE scheme, which primarily supports research staff exchanges. Licensing terms would need to be negotiated directly with the coordinator FTK in Germany. Based on available project data, no commercial pricing model has been published.

Can this scale to thousands of users in a real healthcare setting?

The platform went through two full release cycles (first and second releases of all three layers: data management, big data application, and the full platform). However, MSCA-RISE projects focus on research collaboration rather than commercial deployment, so large-scale stress testing may not have been completed. Scaling would likely require additional engineering investment.

Who owns the IP and can we license the technology?

IP from MSCA-RISE projects typically stays with the institutions that generated it. The consortium includes 3 SMEs and 3 universities across Germany, Ireland, Italy, and the UK. Any licensing arrangement would need to involve the relevant IP holders, starting with coordinator FTK-Forschungsinstitut für Telekommunikation in Germany.

Does this comply with healthcare data regulations like GDPR?

The project handles sensitive health data from smart sensors and chatbot conversations, which falls under strict GDPR and medical data requirements. Based on available project data, the platform includes a dedicated Data and Knowledge Management layer, but specific compliance certifications are not documented in the deliverable titles.

How long would integration into our existing systems take?

The platform architecture has three distinct layers (data management, big data application, and user interaction), each with documented software releases. This modular design suggests integration is feasible, but the MSCA-RISE funding scheme means the focus was on research exchange rather than commercial-ready APIs. Expect additional development work for production integration.

What makes this different from existing nutrition apps?

Unlike consumer nutrition apps, this platform fuses data from multiple smart sensor streams with chatbot interactions and applies machine learning for sophisticated analysis accessible to healthcare professionals. The gamification approach, including a 'Dorian Gray mirror' concept that visualizes health impacts of food choices, goes beyond simple calorie counting.

Is there evidence this actually changes patient behavior?

The project objective describes validation of the platform including the gamification approach. Based on available project data, 11 total deliverables were produced over the 4-year project period. However, specific clinical outcome data or behavior change metrics are not available in the deliverable titles provided.

Consortium

Who built it

The 7-partner consortium across Germany, Ireland, Italy, and the UK brings a balanced mix of 3 industry players (including 3 SMEs) and 4 academic/research organizations, giving a 43% industry ratio. This is unusually high for an MSCA-RISE project and signals genuine commercial interest beyond pure research. The coordinator FTK is a German research institute specializing in telecommunications and cooperation — a solid technical lead for a sensor-and-chatbot platform. The presence of 3 SMEs suggests smaller, agile companies were directly involved in building the technology, which typically means faster paths to market than purely academic consortia. For a business looking to adopt this technology, the multi-country team means potential access to different European healthcare markets and regulatory expertise.

How to reach the team

FTK-Forschungsinstitut für Telekommunikation und Kooperation in Germany led this project. SciTransfer can facilitate a direct introduction to discuss licensing or partnership opportunities.

Next steps

Talk to the team behind this work.

Want to explore how this AI-powered obesity management platform could fit your healthcare product or service? SciTransfer connects businesses with EU research teams — contact us for a tailored introduction.

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