The OActive scientific and technological objectives focus on the developmentobjectives

of patient-specific computer models and simulation in order to develop appropriate

OA prevention interventions or treatments.

 

The main focus of the OActive will be on knee OA (KOA) because this is the joint

where OA symptoms most frequently cause significant loss of function and mobility.

The project objectives include:

Mechanistic modelling framework of the musculoskeletal system

  • Development of personalized neuromusculoskeletal models that could be used to predict knee OA onset and improve treatment
  • Development of novel calibration pipelines for the transformation of generic musculoskeletal models to personalized models by scaling anatomic geometry, kinematics and muscle kinetics and activation parameters.
  • Development of organ and tissue level models for the incorporation of detailed bone and cartilage models capable of predicting tissue responses following estimation of forces from the rigid body musculoskeletal models.

Systemic health and inflammation modelling framework

  • Development of a system of prognostic biomarkers of bone and cartilage degradation and synthesis applied to OA based on serum markers.
  • Development of a system of inflammatory prognostic biomarkers for OA monitoring based on biofluid samples (blood, urine and synovial fluid).

Behaviour, social, environmental modelling framework

  • Assess and model behaviour of users related to physical activity using flexible platforms of wearable body sensors.
  • Development and implement behaviour analysis to create a set of behaviour models and “normality patterns”.
  • Investigate the effect of socio-economical risk factors

Hypermodelling framework empowered by big data

To develop the hyper-modelling framework of OActive which will include:

  • Data management mechanisms
  • Development of data pre-processing algorithms.
  • Development of data mining techniques.
  • Identification of patient-specific significant risk factors associated with the onset as well as factors related to OA progression using computational efficient Feature Selection algorithms.
  • Development of the ICT deep learning infrastructure.
  • Design and implementation of personalized predictive Decision Support (DS) models.

Ontology-based framework for data /models reusability and sharing

  • To employ model and data encoding and exchange standards for multiscale modelling
  • To develop modular approaches to ensure that self-contained models could be developed and validated independently before being incorporated into a hierarchy of imported models
  • Employ Semantic web technology to make knowledge interpretable by web agents
  • To issue authentication mechanisms (via X.509 certificates) assuring the secure access to data.
  • To employ enhanced replication mechanisms to warrant the integrity of data including the prevention of loss.
  • To ensure a certain k-anonymity using pseudo-anonymization techniques.

Personalised interventions using Augmented Reality (AR)

  • To issue personalized intervention relying on the AR gaming concept.
  • To employ assistive, real-time visual and vibrotactile feedback for OA gait retraining.
  • To calculate of biomechanical indicators for assessment and clinical decision support.
  • To implement personalized stimuli to impact on game task completion performance.
  • To apply the model in knee OA patients to investigate what effect simulated biomechanical treatments have on the mechanical load characteristics in knee joint structures in different groups of knee OA patients.

Technology Validation

To test the OActive system using a comprehensive validation strategy that includes:

  • Clinical studies in human populations for the validation of the efficiency of the non-invasive risk factors.
  • In vitro Clinical trials and
  • Validation in large data registries

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