Amsterdam Movement Sciences Research Institute has since its launch in 2017 had several calls aimed at its research members. The aim of the calls in the period 2017 – 2020 was to stimulate and initiate multidisciplinary research collaboration between researchers at our partners from Amsterdam UMC, ACTA and VU Amsterdam, to further develop the research collaboration between departments and partners.

In 2025 we launched the following internal call for a 3-year PPP program:

RehabAI@Home is a collaborative research program, combining rehabilitation and movement sciences with AI and entrepreneurship, to enhance rehabilitation at home. The partners include Vrije Universiteit Amsterdam (VU), Amsterdam University Medical Center (AUMC), Amsterdam University of Applied Sciences (AUAS), and ROM InWest. This program is financed by a €1.5 million program fund of Health Holland's Public-Private-Partnership (PPP) program in 2025.
Aging and improved healthcare will lead to more chronic conditions in the coming decades, resulting in physical limitations, reduced quality of life, high healthcare costs, and increased pressure on less healthcare staff. Rehabilitation is key to reducing the care burden of chronic conditions and keeping people healthier at home. However both the production and the use of rehabilitation innovations stays behind. Introducing user-friendly, affordable, and scalable AI-based innovations could help address this gap in innovation and care.

Goal and approach of RehabAI@Home

RehabAI@Home is aimed to develop innovative technologies that empower people towards a more effective and efficient rehabilitation with self-management support. To this end, researchers within the consortium work in close collaboration with companies towards AI-driven innovations in the rehabilitation sector that are applicable in the home setting of the end users.
Within this program, we set out a call for innovation projects that focus on the validity and implementability of the intended solutions, with attention to usability, interoperability, self-management support, training and education, scalability and flexibility, in addition to privacy and data security, ethical and social acceptance and evaluation. We received nine high-quality applications from collaborative consortia, all of which received high scores from the independent review committee. From these positively rated proposals, we selected the following three projects for funding within our program.

RehabAI@Home projects

trAIn@home: Smart and personalized AI coaching for home rehabilitation in people with acquired brain injury
This project aims to design, integrate, and validate an AI-driven coaching platform for real-time, personalized, and safety-bounded home rehabilitation in adults with acquired brain injury (ABI), such as stroke. Adults with ABI often require long-term rehabilitation at home, but current home training is unstructured and poorly monitored, while therapists face increasing workload and staff shortages. The platform uses multimodal data from wearables, contextual information, and patient-reported outcomes to generate actionable insights, while built-in safety rules and professional oversight ensure reliability and clinical acceptance. Rather than replacing therapists, trAIn@home strengthens their role by providing structured information and guiding patients to exercise safely and effectively at home.

The project follows four staged work packages, progressing from prototype development in controlled settings to usability and system validation in living-lab and larger-scale environments, with continuous co-creation to support adoption. By reducing unnecessary clinic visits and improving therapist time allocation, the platform aims to lower costs and ease healthcare pressure while enhancing patient recovery, autonomy, and safety. It also advances explainable AI integration and delivers validated datasets to improve understanding of rehabilitation outcomes.

AAA RehabTwin: An AI-powered smart coach for personalized stroke rehabilitation
To improve movement quality, adaptability and adherence in home-based rehabilitation programs for stroke patients, this project uses Advanced Sensing (AS), Artificial Intelligence (AI), and Augmented Feedback (AF). The developed system, AAA RehabTwin, will integrate wearable motion sensors, AI-based movement and experience analysis, and interactive patient interfaces to provide real-time joint-level corrections and adaptive feedback and motivational support. In addition to guiding patients, the system enables therapists to remotely monitor progress and adjust rehabilitation plans based on individual recovery trajectories. Built-in monitoring tools enable therapists to remotely track patient progress and personalize rehabilitation plans based on individual recovery trajectories.

The project addresses key limitations of current home rehabilitation, including incorrect exercise execution, risk of physical overload, and reduced motivation due to limited therapist interaction. By combining advanced sensing, artificial intelligence, and augmented feedback, AAA RehabTwin aims to provide safe, accurate, and engaging guidance for patients while strengthening therapist oversight. Ultimately, the project seeks to improve rehabilitation outcomes, support self-directed recovery at home, and advance the integration of AI-driven eRehabilitation solutions into clinical practice.

  • Researchers: Bin Yu (HvA), Thomas Janssen (VU);
  • Company: wearm.ai - specializes in AI-powered wearable sensing for movement analysis;
  • Project priod: 1/2/2026 – 31/1/2029.

OPRAH2.0: Optimal Physical Recovery After Hospitalization
This project focuses on individuals with cancer undergoing major oncological surgery and aims to support their physical activity and recovery at home using an activity monitor, a smartphone application, and remote guidance by physiotherapists and dietitians. The system integrates real-time symptom reporting, multimodal data analysis, and a conversational AI interface to provide tailored feedback, recommendations, and multilingual 24/7 support for patients with diverse literacy and language skills. It combines wearable-derived activity data with patient-reported symptoms such as pain and fatigue to assess both movement quantity and movement quality, enabling individualized, evidence-based guidance. The project progresses through co-creation, technical development, and clinical evaluation within surgical care pathways, aiming for readiness for broader deployment. Ultimately, OPRAH 2.0 seeks to enhance recovery, empower patients, reduce healthcare workload, and establish a scalable, interoperable AI rehabilitation solution that can be extended to other patient groups and care settings.

    Program governance

    The program steering committee (Mirjam Pijnappels (VU), Carel Meskers (AUMC), Bart Visser (AUAS), Linze Rijswijk (ROM InWest), and Paul de Vries (VU IXA-GO)) monitors the projects' alignment with the objectives through reports and six-monthly program meetings. During these meetings, the projects will consult with each other to discuss their progress and any questions or suggestions regarding social, economic, and scientific impact. Stakeholders and the advisory committee will participate in these interproject meetings. The first meeting, with opportunities for questions, exchanges, and discussion, will take place during the kick-off on the 4th of June 2026.