Virtual, physiological and computational neuromuscular models for the predictive treatment of Parkinson's disease.
NoTremor aims to provide patient specific computational models of the coupled brain and neuromuscular systems that will be subsequently used to improve the quality of analysis, prediction and progression of Parkinson’s disease. In particular, it aspires to establish the neglected link between brain modeling and neuromuscular systems that will result in a holistic representation of the physiology for PD patients.
Research Assistant: Integration of neuromusculoskeletal and brain models (spiking neural network) to estimate and simulate stages of Parkinson's disease
Duration: 09/2014 - 12/2016
Funded Under: FP7-ICT (Grant Agreement No. 610391)
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Anal​ysis, modeling and sensing of both physiological and environmental factors for the customized and predictive self-management of asthma.
MyAirCoach aims to develop a holistic mHealth personalized asthma monitoring system empowering patients to manage their own health by providing user-friendly tools to increase the awareness of their clinical state and effectiveness of medical treatment.
Research Assistant: Signal processing and statistical analysis of asthma related indicators
Duration: 01/2017 -12/2017
Funded Under: Horizon 2020 (Grant Agreement No. 643607)


Advanced personalized, multi-scale computer models preventing osteoarthritis.
OActive targets patient-specific OA prediction and interventions by using a combination of mechanistic computational models, simulations and big data analytic. Once constructed, these models will be used to simulate and predict optimal treatments, better diagnostics, and improved patient outcomes, overcoming the limitation of the current treatment interventions, Augmented Reality (AR) empowered interventions will be developed in a personalized framework allowing patients to experience the treatment as more enjoyable, resulting in greater motivation, engagement, and training adherence.
Postdoctoral Researcher: Development of subject specific (MRI segmentation) multibody and finite element models of the knee complex
Duration: 12/2017 - 08/2019
Funded Under: Horizon 2020 (Grant Agreement No. 777159)


Decision support software for anterior cruciate ligament reconstruction based on individualized musculoskeletal computer models.
SafeACL project aims to develop an innovative decision support system based on the integration of neuromusculoskeletal computer models with imaging (MRI, X-ray, ultrasound) and motion analysis data (kinematics, kinetics) to simulate the surgery and to improve customization, objectivity, and ultimately effectiveness of treatments for Anterior Cruciate Ligament (ACL) reconstruction. The SafeACL system will allow the surgeon to operate in a virtual environment using an individualized neuromusculoskeletal model that will be able to predict the functional effect of surgery. In this way, the therapist will be able to test a variety of surgical scenarios (bone channel location, initial graft trend, graft fixation methodology, relative movement between the bone canal and graft) before performing the real surgery. Then, the personalized surgical plan will be fed into a surgical assistive system, which will guide the surgeon to reproduce the selected invasive scenario during the actual surgery.
Postdoctoral Researcher: Development of subject specific multi-scale knee models and predictive simulation schemes
Duration: 07/2018 - 08/2019
Funded Under: Operational Programme Competitiveness, Entrepreneurship and Innovation 2014-2020


The overall goal of the project is to develop, through a combination of machine learning and neuromechanical simulation, accurate models of human locomotion.
The SimGait project is a four year project funded by the SNSF, the Swiss national science foundation (collaborative Sinergia project). This project is a collaboration with Dr Stéphane Armand at the Willy Taillard Kinesiology Lab at the University Hospital of Geneva (HUG) and Prof Alexandros Kalousis of the Data Mining and Machine Learning Group at the University of Applied Sciences, Western Switzerland, in Geneva. The aim of this project is to create a musculoskeletal model of the human with the neural control to be able to model healthy and impaired gait, for example due to cerebral palsy. The model consist of a dynamic model that models the motion of the legs and trunk, and is operated by muscle forces. The height and weight can be scaled to individual persons. The neural control consists of three levels. The first level is reflexes, which are spinal sensorimotor loops to the muscles that do not go through the brain. The second level is the central pattern generator in the spinal cord, which interacts with reflexes and creates time dependent signals to the leg muscles that generate a walking motion. The third level are descending signals from the brain, for example to modulate the speed, or step frequency of the gait. This model will be used to model gaits of persons with cerebral palsy. The goal is to increase our understanding of cerebral palsy by finding which parts of the neural control and muscles are impaired. A second goal is to investigate whether the model can be used to predict the effect of a surgery, such that surgeons can improve the success rate of surgeries.
Postdoctoral Researcher: Development of subject-specific musculoskeletal model and locomotion neural controller of patients with cerebral palsy. Establish methods for identification of possible reflex pathways from direct measurements. Solve forward-driven predictive simulations.
Duration: 09/2019 - Ongoing
Funded Under: SNSF, the Swiss national science foundation (collaborative Sinergia project)