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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004367naa a2200481 4500
001oai:DiVA.org:lnu-119183
003SwePub
008230208s2019 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-1191832 URI
0247 a https://doi.org/10.3390/s190407662 DOI
040 a (SwePub)lnu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Ghayvat, Hemantu Fudan University, China4 aut
2451 0a Smart Aging System :b Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection
264 c 2019-02-13
264 1b 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 7a MEDICIN OCH HÄLSOVETENSKAPx Annan medicin och hälsovetenskapx Gerontologi, medicinsk/hälsovetenskaplig inriktning0 (SwePub)305022 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Other Medical and Health Sciencesx Gerontology, specialising in Medical and Health Sciences0 (SwePub)305022 hsv//eng
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
653 a Health Informatics
653 a Hälsoinformatik
7001 a Awais, Muhammadu Fudan University, China4 aut
7001 a Pandya, Sharnil,c Researcher,d 1984-u Navrachana University, India,AiHealth ; DISA ; DISA-IDP4 aut0 (Swepub:lnu)shpaaa
7001 a Ren, Haou Fudan University, China4 aut
7001 a Akbarzadeh, Saeedu Fudan University, China4 aut
7001 a Chandra Mukhopadhyay, Subhasu Macquarie University, Australia4 aut
7001 a Chen, Chenu Fudan University, China4 aut
7001 a Gope, Prosantau Macquarie University, Australia4 aut
7001 a Chouhan, Arpitau Fudan University, China4 aut
7001 a Chen, Weiu Fudan University, China4 aut
7102 a Fudan University, Chinab Navrachana University, India4 org
7730 t Sensorsd : MDPIg 19:4q 19:4x 1424-8220
8564 u https://doi.org/10.3390/s19040766y Fulltext
8564 u https://lnu.diva-portal.org/smash/get/diva2:1735270/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://www.mdpi.com/1424-8220/19/4/766/pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-119183
8564 8u https://doi.org/10.3390/s19040766

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