{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T00:32:18Z","timestamp":1793665938929,"version":"4.5.1"},"reference-count":152,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T00:00:00Z","timestamp":1582934400000},"content-version":"am","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"name":"NIH","award":["U54DK10255603"],"award-info":[{"award-number":["U54DK10255603"]}]},{"name":"NIH","award":["R01DK11018602"],"award-info":[{"award-number":["R01DK11018602"]}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Current Opinion in Biomedical Engineering"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1016\/j.cobme.2019.01.001","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T06:52:43Z","timestamp":1548744763000},"page":"28-46","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":156,"special_numbering":"C","title":["Windows into human health through wearables data analytics"],"prefix":"10.1016","volume":"9","author":[{"given":"Daniel R.","family":"Witt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-9466","authenticated-orcid":false,"given":"Ryan A.","family":"Kellogg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael P.","family":"Snyder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessilyn","family":"Dunn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cobme.2019.01.001_bib1","article-title":"Defining a mobile health roadmap for cardiovascular health and disease","volume":"5","author":"Eapen","year":"2016","journal-title":"J\u00a0Am Heart Assoc"},{"key":"10.1016\/j.cobme.2019.01.001_bib2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/nrcardio.2015.34","article-title":"The mobile revolution--using smartphone apps to prevent cardiovascular disease","volume":"12","author":"Neubeck","year":"2015","journal-title":"Nat Rev Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib3","series-title":"MHealth: new horizons for health through mobile technologies","author":"eHealth","year":"2011"},{"key":"10.1016\/j.cobme.2019.01.001_bib4","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/nbt.3223","article-title":"The digital phenotype","volume":"33","author":"Jain","year":"2015","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.cobme.2019.01.001_sref5","doi-asserted-by":"crossref","first-page":"e2001402","DOI":"10.1371\/journal.pbio.2001402","article-title":"Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information","volume":"15","author":"Li","year":"2017","journal-title":"PLoS Biol"},{"key":"10.1016\/j.cobme.2019.01.001_sref6","doi-asserted-by":"crossref","first-page":"e0169649","DOI":"10.1371\/journal.pone.0169649","article-title":"Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study","volume":"12","author":"Doherty","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib7","doi-asserted-by":"crossref","first-page":"e115915","DOI":"10.1371\/journal.pone.0115915","article-title":"Population-referenced percentiles for waist-worn accelerometer-derived total activity counts in U.S. youth: 2003 - 2006 NHANES","volume":"9","author":"Wolff-Hughes","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib8","unstructured":"All of Us Research Program Protocol, National Institutes of Health (NIH), All of Us. [cited 2018 11\/9]; Available from: https:\/\/allofus.nih.gov\/about\/all-us-research-program- protocol."},{"key":"10.1016\/j.cobme.2019.01.001_bib9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/547013a","article-title":"Google spin-off deploys wearable electronics for huge health study","volume":"547","author":"Maxmen","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.cobme.2019.01.001_bib10","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/JBHI.2015.2461555","article-title":"An emerging era in the management of Parkinson's disease: wearable technologies and the internet of things","volume":"19","author":"Pasluosta","year":"2015","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.cobme.2019.01.001_bib11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.2217\/pme-2018-0044","article-title":"Wearables and the medical revolution","volume":"15","author":"Dunn","year":"2018","journal-title":"Per Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib12","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1111\/jcpp.12520","article-title":"Actigraph measures discriminate pediatric bipolar disorder from attention-deficit\/hyperactivity disorder and typically developing controls","volume":"57","author":"Faedda","year":"2016","journal-title":"J\u00a0Child Psychol Psychiatry"},{"key":"10.1016\/j.cobme.2019.01.001_bib13","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jad.2016.11.008","article-title":"Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: a pilot study","volume":"209","author":"Gentili","year":"2017","journal-title":"J\u00a0Affect Disord"},{"key":"10.1016\/j.cobme.2019.01.001_bib14","doi-asserted-by":"crossref","first-page":"e74979","DOI":"10.1371\/journal.pone.0074979","article-title":"Co-variation of depressive mood and locomotor dynamics evaluated by ecological momentary assessment in healthy humans","volume":"8","author":"Kim","year":"2013","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib15","first-page":"562","article-title":"Wearable depression monitoring system with heart-rate variability","volume":"2014","author":"Roh","year":"2014","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_bib16","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s13181-014-0439-7","article-title":"Real-time mobile detection of drug use with wearable biosensors: a pilot study","volume":"11","author":"Carreiro","year":"2015","journal-title":"J\u00a0Med Toxicol"},{"key":"10.1016\/j.cobme.2019.01.001_bib17","unstructured":"Search of: wearable: Completed Studies - Results by Topic. ClinicalTrials.gov. [cited 2018 11\/9]; Available from: https:\/\/clinicaltrials.gov\/ct2\/results\/browse?term=wearable&recrs=e&brwse=cond_alpha_all."},{"key":"10.1016\/j.cobme.2019.01.001_bib18","doi-asserted-by":"crossref","DOI":"10.3390\/s17030529","article-title":"A\u00a0comprehensive analysis on wearable acceleration sensors in human activity recognition","volume":"17","author":"Janidarmian","year":"2017","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib19","first-page":"15","article-title":"Wearable biosensing: signal processing and communication architectures issues","author":"Celka","year":"2005","journal-title":"Journal of Telecommunications and Information Technology"},{"key":"10.1016\/j.cobme.2019.01.001_sref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.medengphy.2016.12.011","article-title":"Application of data fusion techniques and technologies for wearable health monitoring","volume":"42","author":"King","year":"2017","journal-title":"Med Eng Phys"},{"key":"10.1016\/j.cobme.2019.01.001_bib20_a","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4137\/BII.S31559","article-title":"Big data application in biomedical research and health care: a literature review","volume":"8","author":"Luo","year":"2016","journal-title":"Biomed Inform Insights"},{"key":"10.1016\/j.cobme.2019.01.001_bib21","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"key":"10.1016\/j.cobme.2019.01.001_bib22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.cobme.2019.01.001_bib23","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure-activity relationships","volume":"55","author":"Ma","year":"2015","journal-title":"J\u00a0Chem Inf Model"},{"key":"10.1016\/j.cobme.2019.01.001_bib24","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/nature12346","article-title":"Connectomic reconstruction of the inner plexiform layer in the mouse retina","volume":"500","author":"Helmstaedter","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.cobme.2019.01.001_bib25","doi-asserted-by":"crossref","first-page":"i121","DOI":"10.1093\/bioinformatics\/btu277","article-title":"Deep learning of the tissue-regulated splicing code","volume":"30","author":"Leung","year":"2014","journal-title":"Bioinformatics"},{"key":"10.1016\/j.cobme.2019.01.001_bib26","doi-asserted-by":"crossref","first-page":"1254806","DOI":"10.1126\/science.1254806","article-title":"RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease","volume":"347","author":"Xiong","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.cobme.2019.01.001_bib27","first-page":"1090","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"10.1016\/j.cobme.2019.01.001_bib28","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.cobme.2019.01.001_bib29","first-page":"1799","article-title":"Joint training of a convolutional network and a graphical model for human pose estimation","volume":"27","author":"Tompson","year":"2014","journal-title":"Proc Adv Neural Inform Proc Syst"},{"key":"10.1016\/j.cobme.2019.01.001_bib30","first-page":"196","article-title":"Strategies for training large scale neural network language models","author":"Mikolov","year":"2011","journal-title":"Proc Automat Speech Recogn Understand"},{"key":"10.1016\/j.cobme.2019.01.001_bib31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process Mag"},{"key":"10.1016\/j.cobme.2019.01.001_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.cobme.2019.01.001_sref34","doi-asserted-by":"crossref","first-page":"e0180318","DOI":"10.1371\/journal.pone.0180318","article-title":"Validation of accuracy of SVM-based fall detection system using real- world fall and non-fall datasets","volume":"12","author":"Aziz","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2174\/1874120700903010001","article-title":"A\u00a0fall and near-fall assessment and evaluation system","volume":"3","author":"Dinh","year":"2009","journal-title":"Open Biomed Eng J"},{"key":"10.1016\/j.cobme.2019.01.001_bib35","article-title":"Real-time classification of patients with balance disorders vs. Normal subjects using a low-cost small wireless wearable gait sensor","volume":"6","author":"Nukala","year":"2016","journal-title":"Biosensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib36","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/TBME.2012.2228262","article-title":"Automatic monocular system for human fall detection based on variations in silhouette area","volume":"60","author":"Mirmahboub","year":"2013","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.cobme.2019.01.001_bib37","doi-asserted-by":"crossref","DOI":"10.3390\/s17061229","article-title":"Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors","volume":"17","author":"Xi","year":"2017","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib38","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting falls with wearable sensors using machine learning techniques","volume":"14","author":"Ozdemir","year":"2014","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib39","first-page":"3461","article-title":"Automated detection of atrial fibrillation episode using novel heart rate variability features","volume":"2016","author":"Gilani","year":"2016","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_bib40","doi-asserted-by":"crossref","first-page":"094003","DOI":"10.1088\/1361-6579\/aad9e7","article-title":"Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device","volume":"39","author":"Smisek","year":"2018","journal-title":"Physiol Meas"},{"key":"10.1016\/j.cobme.2019.01.001_bib41","first-page":"3394","article-title":"Monitoring and detecting atrial fibrillation using wearable technology","volume":"2016","author":"Nemati","year":"2016","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_sref43","doi-asserted-by":"crossref","first-page":"e004313","DOI":"10.1161\/CIRCHEARTFAILURE.117.004313","article-title":"Novel wearable seismocardiography and machine learning algorithms can assess clinical status of heart failure patients","volume":"11","author":"I Inan","year":"2018","journal-title":"Circ Heart Fail"},{"key":"10.1016\/j.cobme.2019.01.001_bib43","first-page":"61","article-title":"Classification of movement of people with Parkinsons disease using wearable inertial movement units and machine learning","volume":"227","author":"Ireland","year":"2016","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.cobme.2019.01.001_sref45","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1002\/mds.26693","article-title":"Machine learning for large-scale wearable sensor data in Parkinson's disease: concepts, promises, pitfalls, and futures","volume":"31","author":"Kubota","year":"2016","journal-title":"Mov Disord"},{"key":"10.1016\/j.cobme.2019.01.001_bib45","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1007\/s10439-018-2104-9","article-title":"Comparative motor pre-clinical assessment in Parkinson's disease using supervised machine learning approaches","volume":"46","author":"Rovini","year":"2018","journal-title":"Ann Biomed Eng"},{"key":"10.1016\/j.cobme.2019.01.001_bib46","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","article-title":"Personalized nutrition by prediction of glycemic responses","volume":"163","author":"Zeevi","year":"2015","journal-title":"Cell"},{"key":"10.1016\/j.cobme.2019.01.001_sref48","doi-asserted-by":"crossref","first-page":"e2005143","DOI":"10.1371\/journal.pbio.2005143","article-title":"Glucotypes reveal new patterns of glucose dysregulation","volume":"16","author":"Hall","year":"2018","journal-title":"PLoS Biol"},{"key":"10.1016\/j.cobme.2019.01.001_bib48","first-page":"23","article-title":"Aggregated indexing of biomedical time series data","volume":"2012","author":"Woodbridge","year":"2012","journal-title":"Proc IEEE Int Conf Healthc Inform Imag Syst Biol"},{"key":"10.1016\/j.cobme.2019.01.001_bib49","doi-asserted-by":"crossref","DOI":"10.1093\/jamia\/ocy121","article-title":"Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data","author":"Barnett","year":"2018","journal-title":"J\u00a0Am Med Inform Assoc"},{"key":"10.1016\/j.cobme.2019.01.001_bib50","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1503\/cmaj.1040750","article-title":"Prescribing exercise as preventive therapy","volume":"174","author":"Warburton","year":"2006","journal-title":"CMAJ"},{"key":"10.1016\/j.cobme.2019.01.001_bib51","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1001\/jama.2018.14854","article-title":"The physical activity guidelines for Americans","volume":"320","author":"Piercy","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.cobme.2019.01.001_bib52","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/s12966-015-0314-1","article-title":"Systematic review of the validity and reliability of consumer-wearable activity trackers","volume":"12","author":"Evenson","year":"2015","journal-title":"Int J Behav Nutr Phys Act"},{"key":"10.1016\/j.cobme.2019.01.001_bib53","article-title":"Pedometer for running activity using accelerometer sensors on the wrist","volume":"3","author":"Ahola","year":"2010","journal-title":"Med Equip Insights"},{"key":"10.1016\/j.cobme.2019.01.001_bib54","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s40279-016-0663-1","article-title":"Step counting: a review of measurement considerations and health-related applications","volume":"47","author":"Bassett","year":"2017","journal-title":"Sports Med"},{"key":"10.1016\/j.cobme.2019.01.001_sref56","doi-asserted-by":"crossref","first-page":"e0118723","DOI":"10.1371\/journal.pone.0118723","article-title":"Step detection and activity recognition accuracy of seven physical activity monitors","volume":"10","author":"Storm","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib56","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1249\/MSS.0000000000001569","article-title":"Video-recorded validation of wearable step counters under free- living conditions","volume":"50","author":"Toth","year":"2018","journal-title":"Med Sci Sports Exerc"},{"key":"10.1016\/j.cobme.2019.01.001_bib57","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1089\/tmj.2015.0120","article-title":"An evaluation of commercial pedometers for monitoring slow walking speed populations","volume":"22","author":"Beevi","year":"2016","journal-title":"Telemed J e Health"},{"key":"10.1016\/j.cobme.2019.01.001_bib58","article-title":"Fitness band accuracy in older community dwelling adults","author":"Madigan","year":"2017","journal-title":"Health Informatics J"},{"key":"10.1016\/j.cobme.2019.01.001_bib59","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.gaitpost.2018.01.035","article-title":"Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica","volume":"61","author":"Chandrasekar","year":"2018","journal-title":"Gait Posture"},{"key":"10.1016\/j.cobme.2019.01.001_bib60","article-title":"Using commercial activity monitors to measure gait in patients with suspected iNPH: implications for ambulatory monitoring","volume":"7","author":"Gaglani","year":"2015","journal-title":"Cureus"},{"key":"10.1016\/j.cobme.2019.01.001_bib61","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/JBHI.2017.2787461","article-title":"Modeling clinically validated physical activity assessments using commodity hardware","volume":"22","author":"Winfree","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.cobme.2019.01.001_bib62","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1186\/s13104-016-2253-6","article-title":"Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents","volume":"9","author":"Schneider","year":"2016","journal-title":"BMC Res Notes"},{"key":"10.1016\/j.cobme.2019.01.001_sref64","doi-asserted-by":"crossref","first-page":"e0170902","DOI":"10.1371\/journal.pone.0170902","article-title":"Evaluation of the Finis Swimsense(R) and the Garmin Swim activity monitors for swimming performance and stroke kinematics analysis","volume":"12","author":"Mooney","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.cobme.2019.01.001_bib64","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1093\/ajcn\/51.2.241","article-title":"A\u00a0new predictive equation for resting energy expenditure in healthy individuals","volume":"51","author":"Mifflin","year":"1990","journal-title":"Am J Clin Nutr"},{"key":"10.1016\/j.cobme.2019.01.001_bib65","series-title":"Essentials of exercise physiology","author":"McArdle","year":"2015"},{"key":"10.1016\/j.cobme.2019.01.001_bib66","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1118\/1.594658","article-title":"Calculation of the average energy absorbed in photon interactions","volume":"7","author":"Cunningham","year":"1980","journal-title":"Med Phys"},{"key":"10.1016\/j.cobme.2019.01.001_bib67","series-title":"A\u00a0biometric study of basal metabolism in man","author":"Harris","year":"1919"},{"key":"10.1016\/j.cobme.2019.01.001_bib68","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1002\/clc.4960130809","article-title":"Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity","volume":"13","author":"Jette","year":"1990","journal-title":"Clin Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib69","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1152\/japplphysiol.00023.2004","article-title":"Metabolic equivalent: one size does not fit all","volume":"99","author":"Byrne","year":"2005","journal-title":"J\u00a0Appl Physiol (1985)"},{"key":"10.1016\/j.cobme.2019.01.001_bib70","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1079\/BJN19890107","article-title":"The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry","volume":"61","author":"Ceesay","year":"1989","journal-title":"Br J Nutr"},{"key":"10.1016\/j.cobme.2019.01.001_bib71","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1038\/oby.2001.48","article-title":"Assessment of free-living physical activity in humans: an overview of currently available and proposed new measures","volume":"9","author":"Schutz","year":"2001","journal-title":"Obes Res"},{"key":"10.1016\/j.cobme.2019.01.001_bib72","doi-asserted-by":"crossref","first-page":"2700212","DOI":"10.1109\/JTEHM.2015.2480082","article-title":"Using smartphone sensors for improving energy expenditure estimation","volume":"3","author":"Pande","year":"2015","journal-title":"IEEE J Transl Eng Health Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib73","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13102-017-0089-6","article-title":"Validity of sports watches when estimating energy expenditure during running","volume":"9","author":"Roos","year":"2017","journal-title":"BMC Sports Sci Med Rehabil"},{"key":"10.1016\/j.cobme.2019.01.001_bib74","doi-asserted-by":"crossref","first-page":"e94","DOI":"10.2196\/mhealth.9754","article-title":"Evaluating the validity of current mainstream wearable devices in fitness tracking under various physical activities: comparative study","volume":"6","author":"Xie","year":"2018","journal-title":"JMIR Mhealth Uhealth"},{"key":"10.1016\/j.cobme.2019.01.001_sref76","article-title":"Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort","volume":"7","author":"Shcherbina","year":"2017","journal-title":"J\u00a0Personalized Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib76","series-title":"A\u00a0New partnership between systems engineering and health care","author":"Reid","year":"2005"},{"key":"10.1016\/j.cobme.2019.01.001_bib77","first-page":"1","article-title":"Characteristics of office-based physician visits, 2014","author":"Ashman","year":"2017","journal-title":"NCHS Data Brief"},{"key":"10.1016\/j.cobme.2019.01.001_bib78","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1161\/CIRCULATIONAHA.113.005119","article-title":"Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study","volume":"129","author":"Chugh","year":"2014","journal-title":"Circulation"},{"key":"10.1016\/j.cobme.2019.01.001_bib79","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1161\/01.STR.22.8.983","article-title":"Atrial fibrillation as an independent risk factor for stroke: the Framingham Study","volume":"22","author":"Wolf","year":"1991","journal-title":"Stroke"},{"key":"10.1016\/j.cobme.2019.01.001_bib80","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1002\/clc.22387","article-title":"Feasibility of extended ambulatory electrocardiogram monitoring to identify silent atrial fibrillation in high-risk patients: the Screening Study for Undiagnosed Atrial Fibrillation (STUDY-AF)","volume":"38","author":"Turakhia","year":"2015","journal-title":"Clin Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib81","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/TBME.2005.855725","article-title":"An automatic beat detection algorithm for pressure signals","volume":"52","author":"Aboy","year":"2005","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.cobme.2019.01.001_bib82","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3399\/bjgp11X548956","article-title":"Screening for atrial fibrillation: sensitivity and specificity of a new methodology","volume":"61","author":"Lewis","year":"2011","journal-title":"Br J Gen Pract"},{"key":"10.1016\/j.cobme.2019.01.001_sref84","doi-asserted-by":"crossref","first-page":"e009351","DOI":"10.1161\/JAHA.118.009351","article-title":"Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist","volume":"7","author":"Bonomi","year":"2018","journal-title":"J\u00a0Am Heart Assoc"},{"key":"10.1016\/j.cobme.2019.01.001_bib84","first-page":"1177","article-title":"Atrial fibrillation detection using a smart phone","volume":"2012","author":"Lee J","year":"2012","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_bib85","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1088\/1361-6579\/aa5dd7","article-title":"Detection of atrial fibrillation episodes using a wristband device","volume":"38","author":"Corino","year":"2017","journal-title":"Physiol Meas"},{"key":"10.1016\/j.cobme.2019.01.001_bib86","first-page":"e199","article-title":"2014 AHA\/ACC\/HRS guideline for the management of patients with atrial fibrillation: a report of the American college of cardiology\/American heart association task force on practice guidelines and the heart rhythm society","volume":"130","author":"January","year":"2014","journal-title":"Circulation"},{"key":"10.1016\/j.cobme.2019.01.001_bib87","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.compbiomed.2015.03.005","article-title":"Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine","volume":"60","author":"Asgari","year":"2015","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib88","first-page":"2102918","article-title":"Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks","volume":"2018","author":"Xu","year":"2018","journal-title":"J\u00a0Healthc Eng"},{"key":"10.1016\/j.cobme.2019.01.001_bib89","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/JBHI.2018.2858789","article-title":"Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings","volume":"22","author":"Fan","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.cobme.2019.01.001_bib90","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.3389\/fphys.2018.01206","article-title":"Automatic detection of atrial fibrillation based on continuous wavelet transform and 2D convolutional neural networks","volume":"9","author":"He","year":"2018","journal-title":"Front Physiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib91","doi-asserted-by":"crossref","first-page":"e129","DOI":"10.2196\/mhealth.7254","article-title":"Clinical validation of heart rate apps: mixed-methods evaluation study","volume":"5","author":"Vandenberk","year":"2017","journal-title":"JMIR Mhealth Uhealth"},{"key":"10.1016\/j.cobme.2019.01.001_sref93","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.hrthm.2018.06.037","article-title":"Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: the iREAD Study","volume":"15","author":"William","year":"2018","journal-title":"Heart Rhythm"},{"key":"10.1016\/j.cobme.2019.01.001_sref94","series-title":"Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO)","author":"Brasier","year":"2018"},{"key":"10.1016\/j.cobme.2019.01.001_bib94","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1160\/TH14-03-0231","article-title":"Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study","volume":"111","author":"Lowres","year":"2014","journal-title":"Thromb Haemost"},{"key":"10.1016\/j.cobme.2019.01.001_bib95","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1016\/j.jacc.2018.03.003","article-title":"Smartwatch algorithm for automated detection of atrial fibrillation","volume":"71","author":"Bumgarner","year":"2018","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib96","first-page":"4931","article-title":"Preliminary assessment of the SensiumVitals(R): a low- cost wireless solution for patient surveillance in the general wards","volume":"2015","author":"Hernandez-Silveira","year":"2015","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_bib97","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.jelectrocard.2014.08.012","article-title":"Clinical validation of a low-power and wearable ECG patch for long term full-disclosure monitoring","volume":"47","author":"Torfs","year":"2014","journal-title":"J\u00a0Electrocardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib98","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1001\/jama.2018.8102","article-title":"Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial","volume":"320","author":"Steinhubl","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.cobme.2019.01.001_bib99","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: a review","volume":"44","author":"Rajendra Acharya","year":"2006","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.cobme.2019.01.001_bib100","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/0002-8703(93)90164-5","article-title":"Heart rate variability and aerobic fitness","volume":"125","author":"De Meersman","year":"1993","journal-title":"Am Heart J"},{"key":"10.1016\/j.cobme.2019.01.001_bib101","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.2337\/diacare.26.5.1553","article-title":"Diabetic autonomic neuropathy","volume":"26","author":"Vinik","year":"2003","journal-title":"Diabetes Care"},{"key":"10.1016\/j.cobme.2019.01.001_bib102","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1161\/01.CIR.98.15.1510","article-title":"Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart)","volume":"98","author":"Nolan","year":"1998","journal-title":"Circulation"},{"key":"10.1016\/j.cobme.2019.01.001_bib103","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/0002-9149(87)90795-8","article-title":"Decreased heart rate variability and its association with increased mortality after acute myocardial infarction","volume":"59","author":"Kleiger","year":"1987","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib104","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/0002-9149(91)90653-3","article-title":"Circadian variation and influence of risk factors on heart rate variability in healthy subjects","volume":"68","author":"Molgaard","year":"1991","journal-title":"Am J Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib105","doi-asserted-by":"crossref","first-page":"G330","DOI":"10.1152\/ajpgi.90488.2008","article-title":"Decreased heart rate variability in patients with cirrhosis relates to the presence and degree of hepatic encephalopathy","volume":"296","author":"Mani","year":"2009","journal-title":"Am J Physiol Gastrointest Liver Physiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib106","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1542\/peds.107.1.97","article-title":"Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis","volume":"107","author":"Griffin","year":"2001","journal-title":"Pediatrics"},{"key":"10.1016\/j.cobme.2019.01.001_bib107","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.ijcard.2017.05.001","article-title":"Heart rate variability to monitor performance in elite athletes: criticalities and avoidable pitfalls","volume":"240","author":"Lucini","year":"2017","journal-title":"Int J Cardiol"},{"key":"10.1016\/j.cobme.2019.01.001_bib108","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1089\/dia.2016.0342","article-title":"Are changes in heart rate variability during hypoglycemia confounded by the presence of cardiovascular autonomic neuropathy in patients with diabetes?","volume":"19","author":"Cichosz","year":"2017","journal-title":"Diabetes Technol Ther"},{"key":"10.1016\/j.cobme.2019.01.001_bib109","first-page":"3225","article-title":"Evaluation of the autonomic nervous system for fall detection","volume":"2009","author":"Nocua","year":"2009","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.cobme.2019.01.001_sref111","doi-asserted-by":"crossref","first-page":"115001","DOI":"10.1088\/1361-6579\/aae1ed","article-title":"Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia","volume":"39","author":"Reinertsen","year":"2018","journal-title":"Physiol Meas"},{"key":"10.1016\/j.cobme.2019.01.001_bib111","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.clineuro.2009.12.015","article-title":"Falls and gait disorders in geriatric neurology","volume":"112","author":"Axer","year":"2010","journal-title":"Clin Neurol Neurosurg"},{"key":"10.1016\/j.cobme.2019.01.001_bib112","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1056\/NEJM199606273342606","article-title":"Persons found in their homes helpless or dead","volume":"334","author":"Gurley","year":"1996","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib113","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.medengphy.2016.10.014","article-title":"Review of fall detection techniques: a data availability perspective","volume":"39","author":"Khan","year":"2017","journal-title":"Med Eng Phys"},{"key":"10.1016\/j.cobme.2019.01.001_bib114","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.medengphy.2014.02.012","article-title":"Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems","volume":"36","author":"Gao","year":"2014","journal-title":"Med Eng Phys"},{"key":"10.1016\/j.cobme.2019.01.001_bib115","doi-asserted-by":"crossref","DOI":"10.3390\/s18051654","article-title":"Deep learning to predict falls in older adults based on daily-life trunk accelerometry","volume":"18","author":"Nait Aicha","year":"2018","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib116","doi-asserted-by":"crossref","DOI":"10.3390\/s16081161","article-title":"An analysis on sensor locations of the human body for wearable fall detection devices: principles and practice","volume":"16","author":"Ozdemir","year":"2016","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib117","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib118","doi-asserted-by":"crossref","DOI":"10.3390\/s18041227","article-title":"A\u00a0novel detection model and its optimal features to classify falls from low- and high-acceleration activities of daily life using an insole sensor system","volume":"18","author":"Cates","year":"2018","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_bib119","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1111\/jgs.13804","article-title":"Real-world accuracy and use of a wearable fall detection device by older adults","volume":"63","author":"Chaudhuri","year":"2015","journal-title":"J\u00a0Am Geriatr Soc"},{"key":"10.1016\/j.cobme.2019.01.001_bib120","first-page":"1","article-title":"Nabil Alshurafa, Wenyao Xu, wearable food intake monitoring technologies: a comprehensive review","volume":"6","author":"Tri Vu","year":"2017","journal-title":"Computers"},{"key":"10.1016\/j.cobme.2019.01.001_bib121","article-title":"A\u00a0wearable earpad sensor for chewing monitoring","author":"Amft","year":"2010","journal-title":"Sensors"},{"key":"10.1016\/j.cobme.2019.01.001_bib122","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TBME.2009.2033037","article-title":"Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior","volume":"57","author":"Sazonov","year":"2010","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.cobme.2019.01.001_bib123","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/j.1365-2869.2004.00433.x","article-title":"Circadian activity rhythm in demented and non-demented nursing- home residents measured by telemetric actigraphy","volume":"14","author":"Paavilainen","year":"2005","journal-title":"J\u00a0Sleep Res"},{"key":"10.1016\/j.cobme.2019.01.001_bib124","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1109\/JBHI.2017.2778271","article-title":"A\u00a0low-power fall detector balancing sensitivity and false alarm rate","volume":"22","author":"Wang","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.cobme.2019.01.001_bib125","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.pmcj.2015.09.007","article-title":"Modeling patterns of activities using activity curves","volume":"28","author":"Dawadi","year":"2016","journal-title":"Pervasive Mob Comput"},{"key":"10.1016\/j.cobme.2019.01.001_bib126","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neunet.2013.01.012","article-title":"Change-point detection in time-series data by relative density-ratio estimation","volume":"43","author":"Liu","year":"2013","journal-title":"Neural Netw"},{"key":"10.1016\/j.cobme.2019.01.001_bib127","series-title":"Advances in knowledge discovery and data mining","first-page":"148","article-title":"Unsupervised change analysis using supervised learning","author":"Hido","year":"2008"},{"key":"10.1016\/j.cobme.2019.01.001_sref129","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s10865-017-9878-3","article-title":"Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention","volume":"41","author":"Korinek","year":"2018","journal-title":"J\u00a0Behav Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib129","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/JBHI.2015.2420680","article-title":"Wearable monitoring of physical functioning and disability changes, circadian rhythms and sleep patterns in nursing home residents","volume":"20","author":"Merilahti","year":"2016","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.cobme.2019.01.001_sref131","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jbi.2017.08.006","article-title":"Monitoring stress with a wrist device using context","volume":"73","author":"Gjoreski","year":"2017","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.cobme.2019.01.001_bib131","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1053\/smrv.1999.0087","article-title":"Computer based sleep recording and analysis","volume":"4","author":"Penzel","year":"2000","journal-title":"Sleep Med Rev"},{"key":"10.1016\/j.cobme.2019.01.001_bib132","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2016.12.004","article-title":"A\u00a0comparative review on sleep stage classification methods in patients and healthy individuals","volume":"140","author":"Boostani","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.cobme.2019.01.001_bib133","series-title":"A\u00a0manual of standardized terminology, techniques and scoring system for sleep stages of human subjects","author":"Kales","year":"1968"},{"key":"10.1016\/j.cobme.2019.01.001_bib134","doi-asserted-by":"crossref","first-page":"597","DOI":"10.5664\/jcsm.2172","article-title":"Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep Apnea definitions task force of the American Academy of sleep medicine","volume":"8","author":"Berry","year":"2012","journal-title":"J\u00a0Clin Sleep Med"},{"key":"10.1016\/j.cobme.2019.01.001_bib135","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cmpb.2011.11.005","article-title":"Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier","volume":"108","author":"Fraiwan","year":"2012","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.cobme.2019.01.001_bib136","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jneumeth.2015.01.022","article-title":"Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines","volume":"250","author":"Lajnef","year":"2015","journal-title":"J\u00a0Neurosci Methods"},{"key":"10.1016\/j.cobme.2019.01.001_bib137","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1088\/1361-6579\/aa9047","article-title":"Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals","volume":"38","author":"Beattie","year":"2017","journal-title":"Physiol Meas"},{"key":"10.1016\/j.cobme.2019.01.001_bib138","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s11325-011-0585-y","article-title":"Movement toward a novel activity monitoring device","volume":"16","author":"Montgomery-Downs","year":"2012","journal-title":"Sleep Breath"},{"key":"10.1016\/j.cobme.2019.01.001_bib139","first-page":"1","article-title":"Validation of fitness tracker for sleep measures in women with asthma","author":"Castner","year":"2018","journal-title":"J\u00a0Asthma"},{"key":"10.1016\/j.cobme.2019.01.001_bib140","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s11325-015-1271-2","article-title":"Fitbit Flex: an unreliable device for longitudinal sleep measures in a non-clinical population","volume":"20","author":"Baroni","year":"2016","journal-title":"Sleep Breath"},{"key":"10.1016\/j.cobme.2019.01.001_bib141","unstructured":"Prevention, C.f.D.C.a, About Underlying Cause of Death 1999\u20132015, CDC WONDER Database 2016. [cited 2018 November 15]; Available from: http:\/\/wonder.cdc.gov\/ucd-icd10.html."},{"key":"10.1016\/j.cobme.2019.01.001_bib142","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.2337\/dc12-2625","article-title":"Economic costs of diabetes in the U.S. in 2012","volume":"36","author":"American Diabetes","year":"2013","journal-title":"Diabetes Care"},{"key":"10.1016\/j.cobme.2019.01.001_bib143","article-title":"Current and emerging technology for continuous glucose monitoring","volume":"17","author":"Chen","year":"2017","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.cobme.2019.01.001_sref145","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2337\/dc16-1525","article-title":"Accuracy and longevity of an implantable continuous glucose sensor in the PRECISE study: a 180-day, prospective, multicenter, pivotal trial","volume":"40","author":"Kropff","year":"2017","journal-title":"Diabetes Care"},{"key":"10.1016\/j.cobme.2019.01.001_bib145","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1089\/dia.2005.7.15","article-title":"The extended Kalman filter for continuous glucose monitoring","volume":"7","author":"Knobbe","year":"2005","journal-title":"Diabetes Technol Ther"},{"key":"10.1016\/j.cobme.2019.01.001_bib146","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1177\/193229681000400222","article-title":"Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms","volume":"4","author":"Bequette","year":"2010","journal-title":"J\u00a0Diabetes Sci Technol"},{"key":"10.1016\/j.cobme.2019.01.001_bib147","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.bbe.2018.02.005","article-title":"Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm","volume":"38","author":"Hamdi","year":"2018","journal-title":"Biocybernetics Biomed Engineering"},{"key":"10.1016\/j.cobme.2019.01.001_sref149","doi-asserted-by":"crossref","DOI":"10.3389\/fams.2017.00014","article-title":"A\u00a0deep learning approach to diabetic blood glucose prediction","volume":"3","author":"Mhaskar","year":"2017","journal-title":"Front. Appl. Math. Stat."},{"key":"10.1016\/j.cobme.2019.01.001_bib149","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.2337\/db11-0654","article-title":"Artificial pancreas: past, present, future","volume":"60","author":"Cobelli","year":"2011","journal-title":"Diabetes"},{"key":"10.1016\/j.cobme.2019.01.001_sref151","doi-asserted-by":"crossref","first-page":"k1310","DOI":"10.1136\/bmj.k1310","article-title":"Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis","volume":"361","author":"Bekiari","year":"2018","journal-title":"BMJ"},{"key":"10.1016\/j.cobme.2019.01.001_bib151","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jbi.2016.07.020","article-title":"Unsupervised detection and analysis of changes in everyday physical activity data","volume":"63","author":"Sprint","year":"2016","journal-title":"J\u00a0Biomed Inform"}],"container-title":["Current Opinion in Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468451118300771?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468451118300771?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T06:58:33Z","timestamp":1762844313000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2468451118300771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3]]},"references-count":152,"alternative-id":["S2468451118300771"],"URL":"https:\/\/doi.org\/10.1016\/j.cobme.2019.01.001","relation":{},"ISSN":["2468-4511"],"issn-type":[{"value":"2468-4511","type":"print"}],"subject":[],"published":{"date-parts":[[2019,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Windows into human health through wearables data analytics","name":"articletitle","label":"Article Title"},{"value":"Current Opinion in Biomedical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cobme.2019.01.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}