Cédric Rose

Learn More
Particle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distributions. The dimensionality of the problem remains a limitation of these approaches due to the growing number of particles required for the exploration of the state space. Computer(More)
— Commercial gait analysis systems rely on wearable sensors. The goal of this study is to develop a low cost marker less human motion capture tool. Our method is based on the estimation of 3d movements using video streams and the projection of a 3d human body model. Dynamic parameters only depend on human body movement constraints. No trained gait model is(More)
Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resources becomes critical with the aging of the population. The(More)
—We present a markerless human motion capture system that estimates the 3D positions of the body joints over time. The system uses a dynamic bayesian network and a factored particle filtering algorithm. In this paper we evaluate the impact of using different observation functions for the bayesian state estimation: chamfer distance, a pixel intersection(More)
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. In this paper we present a decision support system based on a dynamic Bayesian network. Its purpose is to monitor the dry weight of patients suffering(More)
Pharmaceutic studies require to analyze thousands of ECGs in order to evaluate the side effects of a new drug. In this paper we present a new support system based on the use of probabilistic models for automatic ECG segmentation. We used a bayesian HMM clustering algorithm to partition the training base, and we improved the method by using a multi-channel(More)
  • 1