Imagine a care system that can tell how a patient with Alzheimer's or Parkinson's is feeling — just by watching their facial expressions or listening to their voice — and then automatically adjusts the care it provides. That's what TeNDER built: a set of sensors and AI tools that detect a person's mood and connect that information with their medical records to deliver personalized support at home, in daycare, rehabilitation centres, or hospitals. They tested it with over 1,500 people across 4 European regions, including patients, doctors, social workers, and caregivers.
Managing chronic diseases like Alzheimer's, Parkinson's, and cardiovascular conditions requires constant, personalized attention that overwhelms care staff and burns out family caregivers. Current care systems react to crises rather than adapting in real time to how a patient is actually feeling. With 1.2 million Parkinson's patients in Europe alone and cardiovascular disease causing 31% of global deaths, care providers need smarter tools that detect problems before they escalate.
TeNDER built an integrated care platform with three main components: a multi-sensor system that detects patient mood through speech and facial expression analysis, a deep learning recommendation engine that matches patient needs with available care services, and an integration layer connecting sensor data with Electronic Health Records. The final platform was delivered with user guides across 4 deployment scenarios — home, daycare, rehabilitation, and hospital — with the sensorial subsystem code released as open source.
If you are a care home operator dealing with growing demand for personalized dementia and Parkinson's care — this project developed a sensor-based mood detection system tested with 735 patients across 4 European regions that adapts care services automatically based on each resident's emotional state, reducing the burden on your care staff of 570+ caregivers who participated in pilots.
If you are a telehealth company looking to add intelligent chronic disease monitoring — TeNDER built a multi-sensor platform with deep learning that analyzes speech and facial expressions to detect patient mood and activity anomalies, tested across 4 care scenarios from home to hospital. The sensorial subsystem code was released under open source licensing, giving you a ready integration path.
If you are a hospital or rehabilitation centre managing patients with Alzheimer's, Parkinson's, or cardiovascular comorbidities — TeNDER developed an integrated platform that matches patient mood data with Electronic Health Records, piloted with 85 health professionals and 30 social workers across 5 large-scale sites. The system covers 4 care pathways: home, daycare, rehab, and hospital.
The project data does not include specific licensing or implementation costs. However, the sensorial subsystem was released under open source licensing, which could significantly reduce software acquisition costs. Integration and hardware sensor costs would need to be assessed per deployment.
The system was tested in 5 large-scale pilots with 1,500+ end users across 4 European regions, including 735 patients plus a 40% control group (1,030 patients total). This pilot scale suggests the platform architecture can handle multi-site, multi-country deployments.
The sensorial subsystem code (speech analysis, facial expression recognition, activity anomaly detection) was released under open source licensing. The overall TeNDER platform and deep learning recommendation system may have different licensing terms that would need to be discussed with the consortium, led by Universidad Politécnica de Madrid.
The project explicitly addressed data protection, security, and ethical principles as core design requirements. Privacy preservation was built into how clinical data from Electronic Health Records is matched with sensor data. The EuroSciVoc tags include 'data protection' and 'ethical principles'.
The platform was designed to integrate with Electronic Health Records (EHRs) and was deployed across 4 different care scenarios (home, daycare, rehabilitation, hospital). The final integrated version includes user guides and functionality specifications, suggesting a structured deployment process. Based on available project data, exact integration timelines are not specified.
TeNDER ran 5 large-scale pilots involving 85 health professionals, 30 social workers, 570 caregivers, and 735 patients across 4 European regions. The project delivered a final integrated version of all services with complete user guides, plus deep learning-based profile analysis and recommendation systems.
The consortium includes 14 partners across 7 countries, with 5 industry partners and 4 SMEs. The coordinator Universidad Politécnica de Madrid led the project through completion in April 2023. Post-project support availability would need to be confirmed directly with consortium members.
The TeNDER consortium brings together 14 partners from 7 countries (Belgium, Germany, Greece, Spain, Italy, Portugal, Slovenia), led by Universidad Politécnica de Madrid. With 5 industry partners and 4 SMEs making up 36% of the consortium, there is a solid commercial orientation alongside 3 universities and 1 research organization. The geographic spread across Southern and Central Europe, combined with 5 user partners running pilots in 4 regions, means the system has been validated across different healthcare systems and regulatory environments — a significant advantage for any company considering cross-border deployment.
Universidad Politécnica de Madrid (Spain) — contact through SciTransfer for introductions to the research team and technology licensing discussions.
Want to explore how TeNDER's mood-sensing care technology could work in your care facility or health platform? SciTransfer can arrange a direct introduction to the development team and help you evaluate fit.
