A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning.
Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contr...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 4; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142576160&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142576160 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2020 vid: 44 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142576160 142576160 142576160 10.1007/s10916-019-1518-8 142576160 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning. aug: au: Fozoonmayeh, Donya Le, Hai Vu Wittfoth, Ekaterina Geng, Chong Ha, Natalie Wang, Jingjue Vasilenko, Maria Ahn, Yewon Woodbridge, Diane Myung-kyung affil: Data Science, University of San Francisco, San Francisco, CA, USA sug: subj: Medication Compliance Internet of Things Wearable Sensors Machine Learning Cloud Computing Information Systems Human Male Female Adolescence Adult Middle Age Systems Design Mobile Applications Funding Source Algorithms Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contribute to improving medication adherence. The developed Android smartwatch application collects activity sensor data using accelerometer and gyroscope. The cloud-based data pipeline includes distributed data storage, distributed database management system and distributed computing frameworks in order to build a machine learning model which identifies activity types using sensor data. With the proposed sensor data extraction, preprocessing and machine learning algorithms, this study successfully achieved a high F1 score of 0.977 with 13.313 seconds of training time and 0.139 seconds for testing. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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