Project
An IoT infrastructure for monitoring motor fluctuations in Parkinson's disease.
A Brain Research Foundation Verona project on an Internet of Things infrastructure for observing motor fluctuations in Parkinson’s disease, supporting the broader IoT4Care research line on wearable and data-driven movement monitoring.
Period2021 – 2022StatusCompletedFundingBrain Research Foundation Verona O.N.L.U.S.
Objective
This project focused on the infrastructure needed to monitor motor fluctuations in Parkinson’s disease using an Internet of Things approach. Motor behaviour in Parkinson’s disease can vary substantially during the day and in response to therapy; obtaining observations outside an occasional clinical examination is therefore an important prerequisite for longitudinal assessment and for research on personalised assistance.
Within the former CISD site, the project belongs to the Internet of Things 4 Care research line, alongside earlier work on freezing-of-gait prediction and later work on intelligent assistance for Parkinson’s disease.
What the surviving record establishes
The public legacy project record establishes the project identity, its IoT4Care classification, the funding organisation and the exact period 1 June 2021 – 31 May 2022. It records Florenc Demrozi as project referent and identifies Brain Research Foundation Verona O.N.L.U.S. as funder.
The surviving public project page does not describe the sensor configuration, communication architecture, algorithms, clinical protocol or project deliverables. Those details are therefore deliberately not reconstructed from neighbouring publications or from later Parkinson-monitoring work: the site preserves what can be supported by the project record instead of presenting plausible research context as project fact.
Research context
The project sits within a longer Verona research programme on wearable sensing and quantitative assessment of Parkinsonian motor symptoms. Publications from the group around this period study accelerometer-based gait analysis, freezing-of-gait detection and prediction, body-area sensing and activity recognition. They provide useful scientific context, but are linked to this project only when an explicit funding or project acknowledgement is available.