Factored Interval Particle Filtering for Gait Analysis

Scheduled for presentation during the Poster Session "Signal Pattern Classification in Biomedical Signals V" (FrP2A1)International audienceCommercial 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 used which makes this approach generic. The 3d model is characterized by the angular positions of its articulations. The kinematic chain structure allows to factor the state vector representing the conguration of the model. We use a dynamic bayesian network and a modied particle filtering algorithm to estimate the most likely state conguration given an observation sequence. The modied algorithm takes advantage of the factorization of the state vector for efciently weighting and resampling the particles

A Multi-HMM Approach to ECG Segmentation

ISSN: 1082-3409International audiencePharmaceutic 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 approach to automatic ECG segmentation based on hierarchic continuous density hidden Markov models. We applied a wavelet transform to the signals in order to highlight the discontinuities in the modeled ECGs. A training base of standard 12-lead ECGs segmented by cardiologists was used to evaluate the performance of our method. We used a Bayesian HMM clustering algorithm to partition the training base, and we improved the method by using a multi-model approach. We present a statistical analysis of the results where we compare different automatic methods to the segmentation of the cardiologist

Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks

International audienceParticle 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 vision problems such as 3D motion tracking are an example of complex monitoring problems which have a high dimensional state space and observation functions with high computational cost. In this article we focus on reducing the required number of particles in the case of monitoring tasks where the state vector and the observation function can be factored. We introduce a particle filtering algorithm based on the dynamic Bayesian network formalism which takes advantage of a factored representation of the state space for efficiently weighting and selecting the particles. We illustrate the approach on a simulated and a realworld 3D motion tracking tasks

A Support System for ECG Segmentation Based on Hidden Markov Models

Scheduled for presentation during the Poster Session "Signal Pattern Classification in Biomedical Signals V" (FrP2A1)International audiencePharmaceutic 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 segmentation. We present a statistical analysis of the results where we compare different automatic methods to the segmentation of the cardiologist as a gold standard

A Conceptual Graph Model for W3C Resource Description Framework

International audienceWith the aim of building a "Semantic Web" , the content of the documents must be explicitly represented through metadata in order to enable contents-guided search. Our approach is to exploit a standard language (RDF, recommended by W3C) for expressing such metadata and to interpret these metadata in conceptual graphs (CG) in order to exploit querying and inferencing capabilities enabled by CG formalism. The paper presents our mapping of RDF into CG and its interest in the context of the semantic Web

Sleep Activity Recognition using Binary Motion Sensors

International audienceEarly detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect poten- tial sleep disturbances of the monitored senior res- idents. We use an unsupervised inference method based on actigraphy data generated by ambient mo- tion sensors scattered around the senior’s apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates

Une Approche Bayésienne pour la reconnaissance des périodes de sommeil à l'aide de capteurs de mouvement

National audienceLe vieillissement de la population confronte les sociétés modernes à une transformation démographique sans précédent qui ne va pas sans poser de nombreux problèmes. Parmi ceux-ci, il y a le déséquilibre de nos systèmes de retraite, et le coût que va engendrer la prise en charge de la dépendance des plus âgés. Sur ce dernier point, outre les aspects économiques, le placement des personnes âgées n’est bien souvent qu’un choix de raison et peut être assez mal vécu par les personnes. Une réponse à cette problématique sociétale est le développement des technologies qui facilitent le maintien à domicile des personnes âgées. L’état de l’art du domaine regorge de projets amont qui vont dans ce sens. Parmi ceux-ci beaucoup cherchent à développer des systèmes de télésurveillance à domicile. Leurs objectifs sont de détecter, voire de prévenir l’occurrence de situations inquiétantes ou critiques et d’évaluer l’état physique voire la fragilité des personnes suivies. C’est dans ce cadre que se situe cette contribution. Nous nous focaliserons dans cet article sur le problème particulier du suivi de la qualité du sommeil ainsi qu’à la détection des levés nocturnes d’une personne vivant seule à son domicile. Celui-ci est équipé de capteurs ambiants simples tel que des détecteurs de mouvement binaires. Nous présenterons une méthode d’inférence bayésienne qui permet à notre solution d’être assez flexible et robuste aux différents types d’installation et configuration d’appartements tout en maintenant une précision de prédiction de 0.94. Cette solution est en cours de déploiement sur plusieurs dizaines d’appartements en Lorraine

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Les droits disciplinaires des fonctions publiques : « unification », « harmonisation » ou « distanciation ». A propos de la loi du 26 avril 2016 relative à la déontologie et aux droits et obligations des fonctionnaires

The production of tt‾ , W+bb‾ and W+cc‾ is studied in the forward region of proton–proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98±0.02 fb−1 . The W bosons are reconstructed in the decays W→ℓν , where ℓ denotes muon or electron, while the b and c quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions.The production of $t\overline{t}$, $W+b\overline{b}$ and $W+c\overline{c}$ is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 $\pm$ 0.02 \mbox{fb}^{-1}. The $W$ bosons are reconstructed in the decays $W\rightarrow\ell\nu$, where $\ell$ denotes muon or electron, while the $b$ and $c$ quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions