Focus in Ewe

International audience—In this paper, a strides detection algorithm is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides

Robust pedestrian trajectory reconstruction from inertial sensor

International audienceIn this paper, a strides detection algorithm combined with a technique inspired by Zero Velocity Update (ZUPT) is proposed using inertial sensors worn on the ankle. This innovative approach based on a sensors alignment and machine learning can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. As a consequence, the trajectory reconstruction achieves better performances in daily life contexts for example, where a lot of these kinds of strides are performed in narrow areas such as in a house. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle or bicycling, while most algorithms in the literature would wrongly detect strides and produce error in the trajectory reconstruction by generating movements. Our algorithm is evaluated on more than 7800 strides from seven different subjects performing several activities. We validated the trajectory reconstruction during motion capture sessions by analyzing the stride length. Finally, we tested the algorithm in a challenging situation by plotting the computed trajectory on the building map of an 5 hours and 30 minutes office worker recording

Activity recognition from stride detection: a machine learning approach based on geometric patterns and trajectory reconstruction

International audienceIn this paper, an algorithm for activity recognition is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns uses a stride detector that can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides. A technique inspired by Zero Velocity Update is used on the stride detection to compute the trajectory of the device. It allows to compute relevant features for the activity recognition learning task. Compared to most algorithms in the literature, this method does not use fixed-size sliding window that could be too short to provide enough information or too long and leads to overlapping issue when the window covers two different activities

Classification of and risk factors for hematologic complications in a French national cohort of 102 patients with Shwachman-Diamond syndrome.

International audienceBACKGROUND: Patients with the Shwachman-Diamond syndrome often develop hematologic complications. No risk factors for these complications have so far been identified. The aim of this study was to classify the hematologic complications occurring in patients with Shwachman-Diamond syndrome and to investigate the risk factors for these complications. DESIGN AND METHODS: One hundred and two patients with Shwachman-Diamond syndrome, with a median follow-up of 11.6 years, were studied. Major hematologic complications were considered in the case of definitive severe cytopenia (i.e. anemia <7 g/dL or thrombocytopenia <20 × 10(9)/L), classified as malignant (myelodysplasia/leukemia) according to the 2008 World Health Organization classification or as non-malignant. RESULTS: Severe cytopenia was observed in 21 patients and classified as malignant severe cytopenia (n=9), non-malignant severe cytopenia (n=9) and malignant severe cytopenia preceded by non-malignant severe cytopenia (n=3). The 20-year cumulative risk of severe cytopenia was 24.3% (95% confidence interval: 15.3%-38.5%). Young age at first symptoms (<3 months) and low hematologic parameters both at diagnosis of the disease and during the follow-up were associated with severe hematologic complications (P<0.001). Fifteen novel SBDS mutations were identified. Genotype analysis showed no discernible prognostic value. CONCLUSIONS Patients with Shwachman-Diamond syndrome with very early symptoms or cytopenia at diagnosis (even mild anemia or thrombocytopenia) should be considered at a high risk of severe hematologic complications, malignant or non-malignant. Transient severe cytopenia or an indolent cytogenetic clone had no deleterious value

Contribution a l'etude des vibrations et de l'usure des faisceaux de tubes en ecoulement transversal

SIGLEINIST T 75241 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Méthodes topologiques et apprentissage statistique pour l’actimétrie du piéton à partir de données de mouvement

This thesis focuses on the detection of specific movements using ActiMyo, a device developed by the company Sysnav. This system is composed by low-cost miniature inertial sensors that can be worn on the ankle and wrist. In particular, a supervised statistical learning approach aims to detect strides in ankle recordings. This first work, combined with an algorithm patented by Sysnav, allows to compute the trajectory of the pedestrian. This trajectory is then used in a new supervised learning method for the activity recognition, which is valuable information, especially in a medical context. These two algorithms offer an innovative approach based on the alignment of inertial signals and the extraction of candidate intervals which are then classified by the Gradient Boosting Trees algorithm. This thesis also presents a neural network architecture combining convolutional channels and topological data analysis for the detection of movements representative of Parkinson’s disease such as tremors and dyskinesia crises.Cette thèse s’intéresse à la détection de mouvements spécifiques à partir du dispositif ActiMyo développé par la société Sysnav, système de capteurs inertiels miniatures bascoût pouvant se porté à la cheville et au poignet. En particulier, une approche d’apprentissage statistique supervisé vise à détecter les foulées dans les enregistrements cheville. Ce premier travail, combiné avec un algorithme breveté par l’entreprise Sysnav, permet de reconstruire la trajectoire du piéton. Cette trajectoire est ensuite utilisée dans une nouvelle méthode d’apprentissage supervisé pour la reconnaissance d’activité qui est une précieuse information notamment dans un contexte médical. Ces deux algorithmes proposent une approche innovante basée sur l’alignement des signaux inertiels et l’extraction d’intervalles candidats qui sont ensuite classés par l’algorithme de Gradient Boosting Trees. Le manuscrit présente également une architecture de réseaux de neurones combinant des channels de convolution et d’analyse topologique des données pour la détection de mouvements caractéristiques de la maladie de Parkinson tels que les tremblements et crises de dyskinésie

Activity recognition from stride detection: a machine learning approach based on geometric patterns and trajectory reconstruction

International audienceIn this paper, an algorithm for activity recognition is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns uses a stride detector that can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides. A technique inspired by Zero Velocity Update is used on the stride detection to compute the trajectory of the device. It allows to compute relevant features for the activity recognition learning task. Compared to most algorithms in the literature, this method does not use fixed-size sliding window that could be too short to provide enough information or too long and leads to overlapping issue when the window covers two different activities

Robust Stride Detector from Ankle-Mounted Inertial Sensors for Pedestrian Navigation and Activity Recognition with Machine Learning Approaches

International audienceIn this paper, a stride detector algorithm combined with a technique inspired by zero velocity update (ZUPT) is proposed to reconstruct the trajectory of a pedestrian from an ankle-mounted inertial device. This innovative approach is based on sensor alignment and machine learning. It is able to detect 100% of both normal walking strides and more than 97% of atypical strides such as small steps, side steps, and backward walking that existing methods can hardly detect. This approach is also more robust in critical situations, when for example the wearer is sitting and moving the ankle or when the wearer is bicycling (less than two false detected strides per hour on average). As a consequence, the algorithm proposed for trajectory reconstruction achieves much better performances than existing methods for daily life contexts, in particular in narrow areas such as in a house. The computed stride trajectory contains essential information for recognizing the activity (atypical stride, walking, running, and stairs). For this task, we adopt a machine learning approach based on descriptors of these trajectories, which is shown to be robust to a large of variety of gaits. We tested our algorithm on recordings of healthy adults and children, achieving more than 99% success. The algorithm also achieved more than 97% success in challenging situations recorded by children suffering from movement disorders. Compared to most algorithms in the literature, this original method does not use a fixed-size sliding window but infers this last in an adaptive way

Activity recognition from stride detection: a machine learning approach based on geometric patterns and trajectory reconstruction

International audienceIn this paper, an algorithm for activity recognition is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns uses a stride detector that can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides. A technique inspired by Zero Velocity Update is used on the stride detection to compute the trajectory of the device. It allows to compute relevant features for the activity recognition learning task. Compared to most algorithms in the literature, this method does not use fixed-size sliding window that could be too short to provide enough information or too long and leads to overlapping issue when the window covers two different activities