Índice
Literatura académica sobre el tema "Détection et reconnaissance des activités de la vie quotidienne"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Détection et reconnaissance des activités de la vie quotidienne".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Détection et reconnaissance des activités de la vie quotidienne"
Beldame, Yann, Nathalie Pantaléon, Rémi Richard, Hélène Joncheray y Mai-Anh Ngo. "Du sport et du care". Alter 18-3 (2024): 69–86. http://dx.doi.org/10.4000/120st.
Texto completoTesis sobre el tema "Détection et reconnaissance des activités de la vie quotidienne"
Guyot, Patrice. "Caractérisation et reconnaissance de sons d'eau pour le suivi des activités de la vie quotidienne : une approche fondée sur le signal, l'acoustique et la perception". Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2311/.
Texto completoThe analysis of instrumental activities of daily life is an important tool in the early diagnosis of dementia such as Alzheimer. The IMMED project investigates tele-monitoring technologies to support doctors in the diagnostic and follow-up of the illnesses. The project aims to automatically produce indexes to facilitate the doctor’s navigation throughout the individual video recordings. Water sound recognition is very useful to identify everyday activities (e. G. Hygiene, household, cooking, etc. ). Classical methods of sound recognition, based on learning techniques, are ineffective in the context of the IMMED corpus, where data are very heterogeneous. Computational auditory scene analysis provides a theoretical framework for audio event detection in everyday life recordings. We review applications of single or multiple audio event detection in real life. We propose a new system of water flow recognition, based on a new feature called spectral cover. Our system obtains good results on more than seven hours of videos, and thus is integrated to the IMMED framework. A second stage improves the system precision using Gammatone Cepstral Coefficients and Support Vector Machines. However, a perceptive study shows the difficulty to characterize water sounds by a unique definition. To detect other water sounds than water flow, we used material provide by acoustics studies. A liquid sound comes mainly from harmonic vibrations resulting from the entrainment of air bubbles. We depicted an original system to recognize water sounds as group of air bubble sounds. This new system is able to detect a wide variety of water sounds, but cannot replace our water flow detection system. Our two systems seem complementary to provide a robust recognition of different water sounds of daily living. A perceptive study aims to compare our two approaches with human perception. A free categorization task has been set up on various excerpts of liquid sounds. The framework of this experiment encourages causal similarity. Results show several classes of liquids sounds, which may reflect the cognitive categories. In a final experiment performed on these categories, most of the sounds are detected by one of our two systems. This result emphasizes the necessary and sufficient aspect of our two approaches, which seem relevant to characterize and identify a large set of sounds produced by the water
Camier, Thomas Romain. "Détection et reconnaissance des actions simples réalisées par le résident pour l'assistance cognitive". Thèse, Université de Sherbrooke, 2013. http://hdl.handle.net/11143/6541.
Texto completoBouaziz, Ghazi. "Développement et mise en œuvre d'un système de détection de l'isolement social basé sur la reconnaissance des activités en matière de repas et de mobilité chez les personnes âgées à domicile". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES137.
Texto completoThe recognition of daily life activities has been the subject of research for years to provide effective solutions. It is based on the spatio-temporal analysis of situations and behaviors whose input data is information provided by ambient sensors or by sensors worn by the person. This thesis focuses on the instrumentation of the living space by ambient sensors and on the detection of a state of social isolation in elderly people. Two approaches are used to assess social isolation. The first one is based on questionnaires. The second approach is based on the use of technologies for the objective acquisition of data representative of a state, behavior, etc. In particular, the activity "eating" is linked to a sequence of actions such as shopping, cooking, eating and washing dishes. The activity "moving" is linked to mobility within the home and leaving the home. The literature shows that these two activities seem to be relevant for assessing a potential risk of social isolation among older people. The thesis work focuses on four main contributions: A bibliographic review of ADLs detection research to identify its contributions and limitations, and to outline relevant research directions. Specific criteria were chosen to include articles presenting activity detection systems. A system design approach applied to the detection of ADLs. This approach is part of a system engineering process. It describes the analysis of requirements, their modeling through SysML diagrams and the implementation of a hardware and software architecture based on an IoT network. The analysis of ADLs, in our study, uses data from motion detectors and contact sensors. A display on a web application allows you to visualize the results obtained for the caregiver or the family. The original use of four methods to classify ADLs, namely "preparing the meal", "eating the meal", "washing the dishes", "sleeping/relaxing", "hygiene", "the person outside the home", "a visitor inside the home" and "other activities". The first three methods used are K-means, the Gaussian mixture model and BIRCH, which applies weights to the data. Meal-related activities therefore do not have the same weight as the rest of the data, which made it possible to improve the detection of ADLs. The fourth algorithm is based on a logical method following the determination of a correlation matrix using all the available sensors as input. Using the data from the correlation matrix, the algorithm personalizes the detection of meal-related activities by distinguishing a person preparing their meal from a person using a meal delivery service. We validate our methods by referring to the forms filled in by the participants at the beginning and end of the experiment, in which they describe the course of their typical day. These algorithms were applied to an open annotated database to confirm the accuracy of our approaches. The proposal of a score for the level of social isolation of the person being monitored. This score is based on the identification of activities to extract daily habits through behavioral indicators (time spent outside the house and in the kitchen, etc.). Six elderly people were followed for more than 3 months. The logistic regression algorithm was used to extract the level of social isolation, which was compared with the level of social isolation identified using the "Lubben Social Network Scale" questionnaire, which was completed by each participant at the beginning and end of the study
Camier, Thomas Romain. "Détection et identification des activités de la vie quotidienne à l'aide d'un unique microphone par pièce". Mémoire, Université de Sherbrooke, 2011. http://savoirs.usherbrooke.ca/handle/11143/1577.
Texto completoEl, Khadiri Yassine. "Inférence bayésienne pour la détection des activités de la vie quotidienne pour faciliter le maintien à domicile des personnes âgées". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0251.
Texto completoThe increase of the senior population constitutes a major public health issue. The demographic share of the elderly is ever more growing thanks to the progress and advances in medicine and our health care systems. However, with the aging of this population comes a plethora of dependency problems, and this, of course, exponentially.Retirement homes are an expensive and not very popular solution. As a result, we are seeing a surge in home assisted living solutions in the recent years.This topic is in the crossroads between sensor technologies, data transmission, assistance to elderly people and activity monitoring.This thesis explores the application of data analysis algorithms for activity monitoring of elderly people at home. The idea is that with day-to-day monitoring of residents it is possible to infer their autonomy and capacity to perform day-to-day tasks. It also allows caregivers to intervene in cases where the start of some degradation is detected.We explored and adapted some Bayesian inference and time series segmentation methods for activity recognition. And then, we proposed a visualization tool to facilitate the detection of anomalies or changes in everyday habits.This work is part of a CIFRE thesis. The methods and algorithms presented have been put into production and are packaged into Diatelic's the assisted living commercial solution
Zaineb, Liouane. "Services e-santé basés sur la reconnaissance et la prédiction des activités quotidiennes dans les espaces intelligents". Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S109.
Texto completoAdvances in sensor technology and their availability have measured various properties and activities of residents in a smart home. However, obtaining significant knowledge from a large amount of information collected from a sensor network is not a simple task. Due to the complexity of the behavior of the inhabitants, the extraction of meaningful information and the accurate prediction of values representing the future activities of an occupant are research challenges. The main objective of our thesis work is to ensure an efficient analysis of data collected from occupancy sensors in a smart home. In this regard, this work is based on the recognition and evaluation of the daily activities of an elderly person in order to observe, predict and monitor the evolution of his state of dependence, health and to detect by the same occasion, the presence of a loss or a disruption of autonomy in real time
Aïzan, Josky. "Modélisation et reconnaissance d'activités quotidiennes au sein d'une maison intelligente : application à la surveillance des personnes âgées". Thesis, Littoral, 2020. http://www.theses.fr/2020DUNK0557.
Texto completoThe ADL systems for keeping seniors at home are expanding today. The new approaches involve setting up an automated activity monitoring system in a smart home equipped with wearable sensors such as Global Positioning System (GPS), electronics bracelets or RFID chips. These sensors unfortunately have the constraint to be worn constantly. The use of binary sensors is an increasingly common alternative. In this thesis we proposed modeling and recognition of daily activities within a smart home equipped with binary sensors. The first phase of the proposed architecture concerns activity modelling. Deterministic and uncertain sequential pattern mining algorithms were used. These algorothms contain a pre-processing phase that integrates the temporal constraint between events. The performance of these algorithms was evaluated on the MIT database, which contains a collection of human activities from two instruments of 77 and 84 sensors respectively. These experiments show that the number and quality of models from the modeling phase are strongly linked to the confidence rate of the sensors. The second phase of architecture involves the recognition of activities. During this phase, two approaches are proposed. The first approach is to pair the random forest method with the deterministic sequential pattern mining algorithm. This approach incorporates a temporal characterization of the activity models discovered. An experiment is carried out on the MIT database and the results in terms of activity recognition are 98% for the subject 1 and 95% for the subject 2. These results are compared with those in the literature to reflect the performance of the proposed approach. The second approach uses the sequence alignment recognition method based on the Levenshtein distance coupled with the uncertain sequential pattern mining. At this level, the uncertain sequential pattern mining algorithm integrates both the management of time constraints between events and the management of the uncertainty of data from the sensors. The performance of this method was evaluated on the MIT and CASAS databases. The CASAS database contains a collection of data from realistic scenarios to detect normal and intertwined daily activities. The results of the experiments on its two databases show that the recognition rate is an increasing function of the confidente rate of the sensors. These results are 100% and 94% respectively for the normal and interweave activities of the CASAS base and 93% and 90% respectively for the activities of subjects 1 and 2 of the MIT base. Compared with those in literature, these results highlight the effectiveness of our method
Fleury, Anthony. "Détection de motifs temporels dans les environnements multi-perceptifs. Application à la classification automatique des Activités de la Vie Quotidienne d'une personne suivie à domicile par télémédecine". Phd thesis, Grenoble 1, 2008. http://www.theses.fr/2008GRE10160.
Texto completoIn the near 2050, about one third of the French population will be over 65. The works of the team AFIRM of the TIMC-IMAG focus on the monitoring of elderly people at home, to detect, as earlier as possible, a loss of autonomy. This thesis work aims at objectivising criterions as ADL or AGGIR grids by automatically classifying the different Activities of Daily Living performed by the subject during the day. A Health Smart Home is used to do this. Our Smart Home includes, in a real flat, Presence Infra-Red Sensors, (for localisation), door contacts (for the use of some conveniences), temperature and hygrometry sensors in the bathroom and microphones (sound and speech recognition with the GETALP team of the LIG). The subject is also equipped with a kinematic sensor that delivers on the postural transition (by pattern recognition with wavelets) and walk periods (frequency analysis). This manuscript is compound of two major parts. The first one introduces the realisation of the kinematic sensor, its algorithms and its first evaluation; but also the set-up, algorithms and the validation of the other sensors inside the flat and finally the Support Vector Machines algorithms to classify the activities of Daily Living (hygiene, toilets, preparing and having a meal, resting, sleeping, communication and dressing/undressing). The second part deals with the experimental protocol to validate these algorithms and the results of these validations on young and healthy subjects. It introduces the results and a discussion about their validity
Fleury, Anthony. "Détection de motifs temporels dans les environnements multi-perceptifs. Application à la classification automatique des Activités de la Vie Quotidienne d'une personne suivie à domicile par télémédecine". Phd thesis, Université Joseph Fourier (Grenoble), 2008. http://tel.archives-ouvertes.fr/tel-00336400.
Texto completoL'appartement HIS possède des détecteurs de présence infrarouges (localisation), des contacteurs de porte (utilisation de certaines commodités), un capteur de température et d'hygrométrie dans la salle de bains et des microphones (classification des sons/ reconnaissance de la parole avec l'équipe GETALP du LIG). Un capteur cinématique embarqué détecte les transferts posturaux (reconnaissance de formes avec la transformée en ondelettes) et les périodes de marche (analyse fréquentielle).
La première partie de ce manuscrit présente la réalisation du capteur cinématique et les algorithmes associés puis une première validation sur des sujets jeunes suivi de la mise en place et de la validation des autres capteurs de l'appartement HIS et enfin l'algorithme de classification des sept activités de la vie quotidienne reconnues (hygiène, élimination, préparation et prise de repas, repos, habillage/déshabillage, détente et communication), par l'intermédiaire des séparateurs à vaste marge. La seconde partie décrit le protocole expérimental pour valider cette classification et discute de la généralisation des premiers résultats présentés.
Romdhane, Rim. "Reconnaissance d'activités et connaissances incertaines dans les scènes vidéos appliquées à la surveillance de personnes âgées". Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00967943.
Texto completo