Search: onr:"swepub:oai:DiVA.org:lnu-119183" > Smart Aging System :
Fältnamn | Indikatorer | Metadata |
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000 | 04367naa a2200481 4500 | |
001 | oai:DiVA.org:lnu-119183 | |
003 | SwePub | |
008 | 230208s2019 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1191832 URI |
024 | 7 | a https://doi.org/10.3390/s190407662 DOI |
040 | a (SwePub)lnu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Ghayvat, Hemantu Fudan University, China4 aut |
245 | 1 0 | a Smart Aging System :b Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection |
264 | c 2019-02-13 | |
264 | 1 | b MDPI,c 2019 |
338 | a electronic2 rdacarrier | |
520 | a Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% +/- 0.95) from (80.81% +/- 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130). | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Annan medicin och hälsovetenskapx Gerontologi, medicinsk/hälsovetenskaplig inriktning0 (SwePub)305022 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Other Medical and Health Sciencesx Gerontology, specialising in Medical and Health Sciences0 (SwePub)305022 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
653 | a Health Informatics | |
653 | a Hälsoinformatik | |
700 | 1 | a Awais, Muhammadu Fudan University, China4 aut |
700 | 1 | a Pandya, Sharnil,c Researcher,d 1984-u Navrachana University, India,AiHealth ; DISA ; DISA-IDP4 aut0 (Swepub:lnu)shpaaa |
700 | 1 | a Ren, Haou Fudan University, China4 aut |
700 | 1 | a Akbarzadeh, Saeedu Fudan University, China4 aut |
700 | 1 | a Chandra Mukhopadhyay, Subhasu Macquarie University, Australia4 aut |
700 | 1 | a Chen, Chenu Fudan University, China4 aut |
700 | 1 | a Gope, Prosantau Macquarie University, Australia4 aut |
700 | 1 | a Chouhan, Arpitau Fudan University, China4 aut |
700 | 1 | a Chen, Weiu Fudan University, China4 aut |
710 | 2 | a Fudan University, Chinab Navrachana University, India4 org |
773 | 0 | t Sensorsd : MDPIg 19:4q 19:4x 1424-8220 |
856 | 4 | u https://doi.org/10.3390/s19040766y Fulltext |
856 | 4 | u https://lnu.diva-portal.org/smash/get/diva2:1735270/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 | u https://www.mdpi.com/1424-8220/19/4/766/pdf |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-119183 |
856 | 4 8 | u https://doi.org/10.3390/s19040766 |
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