Detecting information flow direction in multivariate linear and nonlinear models

International audienceIn this paper we present an approach to analyze the direction of information flow between time series involving bidirectional relations. The intuitive idea comes from a first study dedicated to the so-called phase slope index, which is a measure originally developed to detect unidirectional relations and is based on the complex coherence function. In order to detect bidirectional flows, we propose two new causality indices supplying the previous index with two other functions, the directed coherence function and the directed transfer function. Moreover, to cope with the inability of the approaches based on coherence (ordinary or directed) or on directed transfer function to distinguish between direct and indirect relations, we propose another causality index based on the partial directed coherence to identify only direct relations. Experimental results show that some challenges have promising solutions through the use of this new indicator dealing with both linear and nonlinear multivariate models

On the evaluation of the conversational speech quality in telecommunications

International audienceIn this paper we propose an objective method to assess speech quality in the conversational context by taking into account the talking and listening speech qualities and the impact of delay. This approach is applied to the results of four subjective tests on the effects of echo, delay, packet loss and noise. The dataset is divided into training and validation sets. For the training set, a multiple linear regression is applied to determine a relationship between conversational, talking and listening speech qualities and the delay value. The multiple linear regression leads to an accurate estimation of the conversational scores with high correlation and low error between subjective and estimated scores, both on the training and validation sets. In addition, a validation is performed on the data of a subjective test found in the literature which confirms the reliability of the regression. The relationship is then applied to an objective level by replacing talking and listening subjective scores with talking and listening objective scores provided by existing objective models, fed by speech signals recorded during the subjective tests. The conversational model achieves high perfor- mance as revealed by comparison with the test results and with the existing standard methodology “E-model”, presented in the ITU-T (International Telecommunication Union) Recommendation G.107

Proposal of a remote monitoring system for elderly health prevention

International audienceThanks to new health sensors and telemedicine, it is possible nowadays to monitor elders' health for better prevention. We propose an architecture for such a system in terms of security, data privacy and user experience. We comply with the French legislation to develop our telemedicine solution. © 2017 IEEE

An efficient machine learning-based fall detection algorithm using local binary features

International audienceAccording to the world health organization, millions of elderly suffer from falls every year. These falls are one of the major causes of death worldwide. As a rapid medical intervention would considerably decrease the serious consequences of such falls, automatic fall detection systems for elderly has become a necessity. In this paper, an efficient machine learning-based fall detection algorithm is proposed. Thanks to the proposed local binary features, this algorithm shows a high accuracy exceeding 99% when tested on a large dataset. In addition, it enjoys an attractive property that the computational cost of decision-making is independent from the complexity of the trained machine. Thus, the proposed algorithm overcomes a critical challenge of designing accurate yet low-cost solutions for wearable fall detectors. The aforementioned property enables implementing autonomous, low-power consumption wearable fall detectors. © EURASIP 2018

Characterizing Peaks in Acceleration Signals-Application to Physical Activity Detection Using Wearable Sensors

International audienceThe world is getting older by the minute due to rising life expectancy, leading to an urgent need for the continuous monitoring of patients. Tracking human activities without hospitalization has been tackled in the past decade thanks to the advancement of sensing technologies and wearable devices. However, the limited power resources of microcontrollers and the power consumption due to the use of different sensors are two issues that make the recognition process via embedded systems an open research topic to date. Consequently, this paper proposes a low-cost machine learning-based human activity recognition algorithm. It detects cyclic activities from wrist-worn tri-axial accelerometer data and classifies them into four classes. Specifically, a novel and smart peak detection technique is proposed, followed by the extraction of small handcrafted feature vectors, representing the input of a novel machine-learning architecture. These three contributions are approved by experimental results on a 3300-file dataset, showing an accuracy of 99.21% with a low computational cost thanks to an efficient implementation for feature extraction. The effectiveness of the proposed recognition process is validated in real world conditions

Wellness sensors and proprietary protocols, a solution for health monitoring?

International audienceTelemedicine and telehomecare allow elders to keep living in their own home in better conditions and without intrusion of privacy. To better monitor elders' health, we propose to develop an automated system that measures different biomedical and environmental parameters. In this context, we first compare standard and proprietary protocols on the one hand and wellness and medical devices on the other hand. Then we deploy our application able to retrieve measurements values from different types of sensors using different protocols

A review of frailty analysis in older adults: from clinical tools towards fully automated preventive systems ⋆

International audienceObjectives: Frailty is a geriatric syndrome characterized by sarcopenia and physiological impairment. Although the majority of older adults wish to age at home, being frail threatens this choice since it increases the risk of falls and loss of functional independence. Hence, frailty screening and early detection are needed to stop or at least slow down the physical weakening process. In this paper, we present a review in which we discuss the proposed methods from the literature that targets frailty detection and analysis, starting from traditional clinical tools then introducing data-driven studies before highlighting the importance of fully automated systems. Material and methods: We conducted a review study by searching several databases such as Google Scholar, IEEE Xplore, MDPI, and ScienceDirect to name a few. This work presents clinical tools and classical performance tests to assess the health status and the physical function, as well as statistical and observational studies to analyze the frailty syndrome. Moreover, we discuss briefly the work of our research team in this context, represented by the development of a telemonitoring system which aims at the transition from a curative to a preventive model. Results: Firstly, this review points out the absence of a gold standard to detect frailty in older individuals. Secondly, it discusses the limitations of self-reported measures/questionnaires and other traditional performance tests which are based on subjective data and done under supervised conditions. Thirdly, our study emphasizes the lack of robust approaches that target the early detection of frailty and the prediction of a future risk of physical worsening. We propose new research directions based, on the one hand, on automatic activity identification and tracking and, on the other hand, on the analysis of spontaneous speech of elderly. Conclusion: This paper describes research findings and highlights the existing gaps in the context of frailty, and serves as a state of the art for researchers. Additionally, this work suggests future research directions regarding the early detection and prevention of frailty

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

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On the identification of relevant degradation indicators in super wideband listening quality assessment models

International audienceRecently, new objective speech quality evaluation methods, designed and adapted to new high voice quality contexts, have been developed. One interest of these methods is that they integrate voice quality perceptual dimensions reflecting the effects of frequency-response distortions, discontinuities, noise and/or speech level deviations respectively. This makes it possible to use these methods also to provide diagnostic information about specific aspects of the transmission systems' quality, as perceived by end-users. In this paper, we present and analyze in depth two of these approaches namely POLQA (Perceived Objective Listening Quality Assessment) and DIAL (Diagnostic Instrumental Assessment of Listening quality), in terms of quality degradation indicators related to the perceptual dimensions these models could embed. The main goal of our work is to find and propose the most robust quality degradation indicators to reliably characterize the impact of degradations relative to the perceptual dimensions described above and to identify the underlying technical causes in super wideband telephone communications [50, 14000] Hz. To do so, the first step of our study was to identify in both models the correspondence between perceptual dimensions and quality degradation indicators. Such indicators could be either present in the model itself or derived from our own investigation of the model. In a second step, we analyzed the performance and robustness of the identified quality degradation indicators on speech samples only impaired by one degradation (representative of one perceptual dimension) at a time. This study highlighted the reliability of some of the quality degradation indicators embedded in the two models under study and stood for a first step in the evaluation of performance of these indicators to quantify the degradation for which they were designed