259 research outputs found

    HABITAT TELEMONITORING SYSTEM BASED ON THE SOUND SURVEILLANCE

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    International audienceThis paper presents a telemonitoring system in an habitat equipped with physiological sensors, position encoders of the person, and microphones. The originality of our approach consists in replacing the video camera monitoring, not well accepted by the patients, with microphones acquiring the sounds. The sounds are analyzed and not stored in order to maintain the person privacy. We present the entire telemonitoring system which makes the data fusion between medical information and sound information and particulary the sound processing algorithms to detect a distress situation. The first step of sound processing is the sound event detection in a noisy everyday life environment. Sound event detection is necessary to extract the significant sounds before initiating the classification step. Sound classification system and its performances are presented in this paper, too. Introduction Medical monitoring is more and more frequently used in order to reduce hospitalisation costs. There are many researches in telemedicine, but few of them are sound based. In this paper, we present a medical telemonitoring system with a smart audio sensor. The system we work on is designed for the surveillance of the elderly, convalescent persons or pregnant women [1]. Its main goal is to detect serious accidents as falls or faintness at any place in the apartment. It was noted that the elderly had difficulties in accepting the video camera monitoring, considering it a violation of their privacy. Thus, the originality of our approach consists in replacing the video camera by a system of multichannel sound acquisition. The system analyzes in real time the sound environment of the apartment and detects the abnormal sounds (falls of objects or patient) and the calls for help, that could indicate a distress situation in the habitat. Again, to respect privacy, no continuous recording or storage of the sound is made, since only the last 5s of the audio signal are kept in a buffer and sent to the alarm monitor if a sound event is detected. The sound information extractio

    Sound Detection and Classification for Medical Telesurvey

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    International audienceMedical Telesurvey needs human operator assistance by smart information systems. This paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the sig-nificant sounds before initiating the classification step. An algorithm based on the Wavelet Transform is evaluated in noisy environment. Then Wavelet based cepstral coeffi-cients are proposed and their results are compared with more classical parameters. Detection algorithm and sound classification methods are applied to medical telemonitor-ing. In our opinion, microphones surveying life sounds are better preserving patient privacy than video cameras

    Life Sounds Extraction and Classification in Noisy Environment

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    International audienceThis paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before ini-tiating the classification step. We present three original event detection algorithms. Among these algorithms, one is based on the wavelet and gives the best performances. We evaluate and compare their performance in a noisy en-vironment with the state of the art algorithms in the field. Then, we present a statistical study to obtain the acous-tical parameters necessary for the training and, the sound classification results. The detection algorithms and sound classification are applied to medical telemonitoring. We re-place video camera by microphones surveying life sounds in order to preserve patient's privacy

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

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    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

    COMMUNICATION BETWEEN A MULTICHANNEL AUDIO ACQUISITION AND AN INFORMATION SYSTEM IN A HEALTH SMART HOME FOR DATA FUSION

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    International audienceThe Health Integrated Smart Home Information System (HIS²) has been developed in the TIMC laboratory for the remote monitoring of the health status of an elderly person during daily life at home. This aims at improving patients' life conditions and at reducing the costs of the long hospitalization. The design of this system is based on a CAN network linked to volumetric, physiological and environment sensors. In addition, a collaboration between the TIMC and the CLIPS laboratories permitted to replace the video camera, not well accepted by the patients by a system based on a multichannel Sound Acquisition. The coupling between both systems will enable to detect if the person is in a situation of distress or not. Both systems locally processe in real time the incoming data and communicate using a CAN network to display the health status. This article describes the system architecture of both systems, practical solutions for their communication and the evaluation results

    Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

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    In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

    Distributed Computing and Monitoring Technologies for Older Patients

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    This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions
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