Home Supporting Smart Systems for Elderly People

Boumpa, Eleni; Kakarountas, Athanasios

doi:10.1007/978-3-030-41368-2_4

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The service is implemented as an online health service (OnHS) and/or an offline health service (OffHS), both involving eHealth sensors used at home. As established in the technology choice phase, in order to meet one of the main requirements of the European AAL initiative in terms of interoperability and open interfaces -to access the European market -the eHealth system concept is oriented towards Open-Source Hardware (OSHW) and COTS (Commercial OF The Shelf) platforms (eHealth, includes COTS biosensors [12]). The SAVE eHealth system is based on the low-cost platform from Libelium, namely the "MySignals" Hardware Development Platform -an eHealth and Medical IoT Development Platform for Arduino.…”

Section: Save -Online Health Servicementioning

confidence: 99%

“SAVE” – An Integrated Approach of Personal and Home Safety for Active Assisted Living

Moraru

Moșoi

Kristály

et al. 2021

IFIP Advances in Information and Communication Technology

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Save -Online Health Servicementioning

confidence: 99%

“SAVE” – An Integrated Approach of Personal and Home Safety for Active Assisted Living

Moraru

Moșoi

Kristály

et al. 2021

IFIP Advances in Information and Communication Technology

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“…Technology can be used to monitor the home and report irregularities in activities and vital signs. Supporting elderly people in daily routines and living within their residence [4]. Technologies can be divided into two subcategories, Invasive and non-invasive [5].…”

Section: Introductionmentioning

confidence: 99%

An Implementation of Real-Time Activity Sensing Using Wi-Fi: Identifying Optimal Machine-Learning Techniques for Performance Evaluation

et al. 2022

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The elderly population is growing, and the health care system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes. Increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time non-invasive sensing systems. This paper makes use of Radio Frequency sensing, where signal propagation is observed in Channel State Information (CSI) reports on Activities of Daily Living (ADLs). Real-time data has been collected for three classifications, "movement", "empty room", and "no activity". A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew and standard deviation features are extracted from the CSI data. A machine learning model provides classification for the real-time monitoring system allowing detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insight into the real-time capabilities of the system. The Random Forest algorithm is chosen to create the machine learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100 % on new unseen testing data with an average classification time of 7.31 milliseconds.

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