Summary
Despite the fact that there has been some progress in developing methods for estimating Parkinson’s disease (PD) symptoms using wearable sensors, these technologies have not sufficiently penetrated the market due to the usability of the system and its cost; existing technologies are constrained by specific hardware requirements or user activities, therefore aversively affecting the cost and usability of the technologies, respectively. Thus, Con-PDmode delivers a software prototype for control-oriented modelling and estimation of PD symptoms that are transparent and platform agnostic. The excellence in innovation potential for successfully bringing the PD assessment tool to the market is the capability to account for inaccuracies and uncertainty in the data collected from low-cost wearable sensors. To this purpose, we will adapt and implement advanced stochastic techniques for improved robustness in state estimation, the confidence level of the model estimation will be transparently communicated to its users. We will perform iterative tests in various environmental and sensor configurations to refine our algorithms as well as the user interface.
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| Web resources: | https://cordis.europa.eu/project/id/899260 |
| Start date: | 01-11-2020 |
| End date: | 30-04-2022 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Despite the fact that there has been some progress in developing methods for estimating Parkinson’s disease (PD) symptoms using wearable sensors, these technologies have not sufficiently penetrated the market due to the usability of the system and its cost; existing technologies are constrained by specific hardware requirements or user activities, therefore aversively affecting the cost and usability of the technologies, respectively. Thus, Con-PDmode delivers a software prototype for control-oriented modelling and estimation of PD symptoms that are transparent and platform agnostic. The excellence in innovation potential for successfully bringing the PD assessment tool to the market is the capability to account for inaccuracies and uncertainty in the data collected from low-cost wearable sensors. To this purpose, we will adapt and implement advanced stochastic techniques for improved robustness in state estimation, the confidence level of the model estimation will be transparently communicated to its users. We will perform iterative tests in various environmental and sensor configurations to refine our algorithms as well as the user interface.Status
CLOSEDCall topic
ERC-2019-POCUpdate Date
27-04-2024
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