{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T16:45:13Z","timestamp":1776962713735,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2014,3,21]],"date-time":"2014-03-21T00:00:00Z","timestamp":1395360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living.  A feasibility study involving N = 10 participants was carried out to evaluate the devices\u2019 ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Na\u00efve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances.<\/jats:p>","DOI":"10.3390\/s140305687","type":"journal-article","created":{"date-parts":[[2014,3,21]],"date-time":"2014-03-21T12:06:20Z","timestamp":1395403580000},"page":"5687-5701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices"],"prefix":"10.3390","volume":"14","author":[{"given":"John","family":"Guiry","sequence":"first","affiliation":[{"name":"Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pepijn","family":"Van de Ven","sequence":"additional","affiliation":[{"name":"Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Nelson","sequence":"additional","affiliation":[{"name":"Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,3,21]]},"reference":[{"key":"ref_1","unstructured":"Favell, A. Global Mobile Statistics 2013 Part A: Mobile Subscribers; Handset Market Share; Mobile Operators. Available online: http:\/\/mobithinking.com\/mobile-marketing-tools\/latest-mobile-stats\/a."},{"key":"ref_2","unstructured":"Kearney, A. The Mobile Economy 2013. Available online: http:\/\/www.atkearney.com\/documents\/10192\/760890\/The_Mobile_Economy_2013.pdf."},{"key":"ref_3","unstructured":"The World in 2013: ICT Facts and Figures. Available online: http:\/\/www.itu.int\/en\/ITU-D\/Statistics\/Documents\/facts\/ICTFactsFigures2013-e.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TNSRE.2010.2070807","article-title":"Barometric pressure and triaxial accelerometry-based falls event detection","volume":"18","author":"Bianchi","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","first-page":"1","article-title":"Design and evaluation of a fall detection algorithm on mobile phone platform","volume":"70","author":"Silva","year":"2011","journal-title":"Ambient Media Syst."},{"key":"ref_6","first-page":"277","article-title":"Fall detection by embedding an accelerometer in cellphone and using KFD algorithm","volume":"6","author":"Zhang","year":"2006","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sposaro, F., and Tyson, G. (2009, January 3\u20136). iFall: An Android application for fall monitoring and response. Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5334912"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1053\/apmr.2001.24893","article-title":"Gait variability and fall risk in community-living older adults: A 1-year prospective study","volume":"82","author":"Hausdorff","year":"2001","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"M446","DOI":"10.1093\/gerona\/58.5.M446","article-title":"Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people","volume":"58","author":"Menz","year":"2003","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0140-6736(12)61031-9","article-title":"Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy","volume":"380","author":"Lee","year":"2012","journal-title":"Lancet"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1161\/CIR.0b013e31820a55f5","article-title":"Forecasting the future of cardiovascular disease in the United States: A policy statement from the American Heart Association","volume":"123","author":"Heidenreich","year":"2011","journal-title":"Circulation"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1089\/pop.2009.12203","article-title":"Distinguishing the economic costs associated with type 1 and type 2 diabetes","volume":"12","author":"Dall","year":"2009","journal-title":"Popul. Health Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1093\/eurheartj\/ehi733","article-title":"Economic burden of cardiovascular diseases in the enlarged European Union","volume":"27","author":"Leal","year":"2006","journal-title":"Eur. Heart J."},{"key":"ref_14","unstructured":"ECB Statistical Data Warehouse. Available online: http:\/\/sdw.ecb.europa.eu\/home.do."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bieber, G., Koldrack, P., Sablowski, C., Peter, C., and Urban, B. (2010, January 23\u201325). Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis. Samos, Greece.","DOI":"10.1145\/1839294.1839354"},{"key":"ref_16","first-page":"289","article-title":"Activity Recognition for Everyday Life on Mobile Phones","volume":"Volume 5615","author":"Stephanidis","year":"2009","journal-title":"Universal Access in Human-Computer Interaction. Intelligent and Ubiquitous Interaction Environments"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, J. (2009, January 23). Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones. Beijing, China.","DOI":"10.1145\/1631040.1631042"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/978-3-642-21535-3_14","article-title":"Physical Activity Monitoring with Mobile Phones","volume":"Volume 6719","author":"Abdulrazak","year":"2011","journal-title":"Toward Useful Services for Elderly and People with Disabilities"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Terrier, P., and Schutz, Y. (2005). How useful is satellite positioning system (GPS) to track gait parameters? A review. J. Neuroeng. Rehabil, 2.","DOI":"10.1186\/1743-0003-2-28"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TITB.2005.856863","article-title":"Activity classification using realistic data from wearable sensors","volume":"10","author":"Parkka","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ganti, R., Srinivasan, S., and Gacic, A. (2010, January 7\u20139). Multisensor fusion in smartphones for lifestyle monitoring. Singapore.","DOI":"10.1109\/BSN.2010.10"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"575","DOI":"10.3390\/s140100575","article-title":"On the capability of smartphones to perform as communication gateways in medical wireless personal area networks","volume":"14","author":"Luque","year":"2014","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12588","DOI":"10.3390\/s120912588","article-title":"Comprehensive context recognizer based on multimodal sensors in a smartphone","volume":"12","author":"Han","year":"2012","journal-title":"Sensors"},{"key":"ref_24","unstructured":"PAL Technologies Ltd. Available online: http:\/\/www.paltech.plus.com\/."},{"key":"ref_25","unstructured":"ActiGraph. Available online: http:\/\/www.actigraphcorp.com\/."},{"key":"ref_26","unstructured":"Philips Actical. Available online: http:\/\/www.healthcare.philips.com\/."},{"key":"ref_27","unstructured":"Shimmer. Available online: http:\/\/www.shimmersensing.com\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1016\/j.medengphy.2008.09.005","article-title":"Direct measurement of human movement by accelerometry","volume":"30","author":"Godfrey","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/S0268-0033(98)00089-8","article-title":"A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument","volume":"13","year":"1998","journal-title":"Clin. Biomech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1152\/japplphysiol.00465.2009","article-title":"An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer","volume":"107","author":"Staudenmayer","year":"2009","journal-title":"J. Appl. Physiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Maurer, U., Smailagic, A., Siewiorek, D.P., and Deisher, M. (2006, January 3\u20135). Activity recognition and monitoring using multiple sensors on different body positions. Cambridge, MA, USA.","DOI":"10.21236\/ADA534437"},{"key":"ref_33","unstructured":"Marsland, S. (2009). Machine Learning: An Algorithmic Perspective, CRC Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/3\/5687\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:09:29Z","timestamp":1760216969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/3\/5687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,3,21]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2014,3]]}},"alternative-id":["s140305687"],"URL":"https:\/\/doi.org\/10.3390\/s140305687","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,3,21]]}}}