Smart sound sensor to detect the number of people in a room

International audienceAmbient sound monitoring is a widely used strategy to follow older adults, which could help them achieve healthy ageing with comfort and security. In a previous work, we have already developed a smart audio sensor able to recognize everyday life sounds in order to detect activities of daily living (ADL) and distress situations. In this paper, we propose to add a new functionality by analyzing the speech flow to detect the number of people in a room. The proposed algorithms are based on speaker diarization methods. This information can be used to better detect activities of daily life but also to know when the person is home alone. This functionality can also offer more comfort through light, heating and air conditioning adaptation to the number of people in an environment

Chapter A Fuzzy Logic Approach for Remote Healthcare Monitoring by Learning and Recognizing Human Activities of Daily Living

Systems analysis & desig

Multi-dimensional profiling of elderly at-risk for Alzheimer's disease in a differential framework

International audienceThe utility of EEG in Alzheimer’s disease (AD) research has been demonstrated over several decades in numerous studies. EEG markers have been employed successfully to investigate AD-related alterations in prodromal AD and AD dementia. Preclinical AD is a recent concept and a novel target for clinical research. This project tackles two issues: first, AD prediction at the preclinical sta ge, by exploiting the multimodal INSIGHT-preAD database, acquired at the Pitié-Salpetrière Hospital; second, an automatic AD diagnosis in a differential framework, by exploiting another large-scale EEG database, acquired at Charles-Foix Hospital. In this project, we will investigate AD predictors at preclinical stage, using EEG data of only subjective Memory Complainers in order to establish a cognitive profiling of elderly at-risk. We will also identify EEG markers for AD detection at early stages in a di fferential diagnosis context. The correlation between EEG markers and clinical biomarkers will be also assessed for a better characterization of the retrieved profiles and a better understanding on the severity of the cognitive disorder. The exploited larg e-scale complementary data offer the opportunity to investigate the full spectrum of the AD neuro-degeneration changes in the brain, using a big data approach and multimodal patient profiling based on resting-state EEG marker

Optimisation de filtres en treillis non stationnaires et etude comparative de filtres multidimensionnels avec application au traitement d'antenne

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Sound event detection in remote health care - Small learning datasets and over constrained Gaussian Mixture Models

International audienceThe use of Gaussian Mixture Models (GMM), adapted through the Expectation Minimization(EM)algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use, at a first glance, is founded on the good qualities of GMM models when aimed at approximating Probability Density Functions(PDF) of random variables. But in some cases, where models are to be adapted from small sample sets of specific and locally recorded signals, instead of large but generic databases, a problem of balance between model complexity and sample size may play an important role. From this perspective, we show, through simple sound classification experiments, that constrained GMM, with fewer degrees of freedom, as compared to GMM with full covariance matrices, provide better classification performances. Moreover, pushing this argument even further, we also show that a Parzen model (seen here as an over-constrained GMM) can do even better than usual GMM, in terms of classification error rati

Chapter Medical Remote Monitoring using sound environment analysis and wearable sensors

Optical physic

Sound environment analysis for ADL detection from Living Lab to Medical Nursing

International audienceIn this paper we present the application of sound environment analysis algorithms previously developed for ADL recognition on real recordings made in a medical nursing home. Initially the sound algorithms were tested in laboratory, secondly in a living lab in Grenoble and lastly in a nursing home (EHPAD). Several days of audio signal have been recorded, but for the moment only 24h are labelized and used for evaluation. The proposed system is based on a combination of Wavelets Transform, Gaussian Mixture Models (GMM) and Suport Vector Machines (SV

Medical Remote Monitoring using sound environment analysis and wearable sensors

Non

SpeechDat-Car: Towards a collection of speech databases for automotive environments

Contains fulltext : 76428.pdf (author's version ) (Open Access)4 p

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process