{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T04:20:11Z","timestamp":1770438011364,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2013,7,17]],"date-time":"2013-07-17T00:00:00Z","timestamp":1374019200000},"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>This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.<\/jats:p>","DOI":"10.3390\/s130709183","type":"journal-article","created":{"date-parts":[[2013,7,17]],"date-time":"2013-07-17T11:57:23Z","timestamp":1374062243000},"page":"9183-9200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":317,"title":["Optimal Placement of Accelerometers for the Detection of Everyday Activities"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-7354","authenticated-orcid":false,"given":"Ian","family":"Cleland","sequence":"first","affiliation":[{"name":"School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim,  Northern Ireland BT37 0QB, UK"}]},{"given":"Basel","family":"Kikhia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering,  Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0882-7902","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim,  Northern Ireland BT37 0QB, UK"}]},{"given":"Andrey","family":"Boytsov","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering,  Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}]},{"given":"Josef","family":"Hallberg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering,  Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-6751","authenticated-orcid":false,"given":"K\u00e5re","family":"Synnes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering,  Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}]},{"given":"Sally","family":"McClean","sequence":"additional","affiliation":[{"name":"Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry,  Northern Ireland BT52 1SA, UK"}]},{"given":"Dewar","family":"Finlay","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim,  Northern Ireland BT37 0QB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2013,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1016\/j.patrec.2008.08.002","article-title":"Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers","volume":"29","author":"Yang","year":"2008","journal-title":"Pattern Recognition Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TBCAS.2011.2160540","article-title":"Sensor positioning for activity recognition using wearable accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/10.554760","article-title":"A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity","volume":"44","author":"Bouten","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/25\/2\/R01","article-title":"Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement","volume":"25","author":"Mathie","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/978-3-642-05167-8_14","article-title":"Biomedical sensors for ambient assisted living","volume":"55","author":"McAdams","year":"2010","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1152\/japplphysiol.00150.2009","article-title":"Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer","volume":"107","author":"Bonomi","year":"2009","journal-title":"J. Appl. Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-540-24646-6_1","article-title":"Activity recognition from user-annotated acceleration data","volume":"3001","author":"Bao","year":"2004","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_8","unstructured":"Olgu\u0131n, D.O., and Pentland, A.S. (2006, January 11\u201314). Human Activity Recognition: Accuracy Across Common Locations for Wearable Sensors. Montreux, Switzerland."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Lustrek, M., and Gams, M. (2011, January 6\u20138). Accelerometer Placement for Posture Recognition and Fall Detection. Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TBME.2008.2006190","article-title":"A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data","volume":"56","author":"Preece","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1016\/j.measurement.2013.03.004","article-title":"Comparison of median frequency between traditional and functional sensor placements during activity monitoring","volume":"46","author":"Bergmann","year":"2013","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S68","DOI":"10.1249\/MSS.0b013e3182399e5b","article-title":"Best practices for using physical activity monitors in population-based research","volume":"44","author":"Matthews","year":"2012","journal-title":"Med. Sci. Sport. Exercise"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1007\/11890348_39","article-title":"Feature selection and activity recognition from wearable sensors","volume":"4239","author":"Pirttikangas","year":"2006","journal-title":"Ubiquitous Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/BF02347551","article-title":"Classification of basic daily movements using a triaxial accelerometer","volume":"42","author":"Mathie","year":"2004","journal-title":"Med. Biolog Eng. Comput."},{"key":"ref_16","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_17","unstructured":"Ravi, N., Dandekar, N., Mysore, P., and Littman, M.L. (2005, January 9\u201313). Activity Recognition from Accelerometer Data. Pittsburgh, PA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1249\/MSS.0b013e3181a24536","article-title":"Detection of type, duration, and intensity of physical activity using an accelerometer","volume":"41","author":"Bonomi","year":"2009","journal-title":"Med. Sci. Sport. Exercise"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yeoh, W.S., Pek, I., Yong, Y.H., Chen, X., and Waluyo, A.B. (2008, January 21\u201324). Ambulatory Monitoring of Human Posture and Walking Speed Using Wearable Accelerometer Sensors. Vancouver, Canada.","DOI":"10.1109\/IEMBS.2008.4650382"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.medengphy.2004.11.006","article-title":"A description of an accelerometer-based mobility monitoring technique","volume":"27","author":"Lyons","year":"2005","journal-title":"Med. Eng. Phys."},{"key":"ref_21","first-page":"311","article-title":"Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers","volume":"7","author":"Parvis","year":"1995","journal-title":"Sensors Mater."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0268-0033(95)00068-2","article-title":"Relationship between vertical ground reaction force and speed during walking, slow jogging, and running","volume":"11","author":"Keller","year":"1996","journal-title":"Clin. Biomech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/30\/4\/R01","article-title":"Activity identification using body-mounted sensors\u2014A review of classification techniques","volume":"30","author":"Preece","year":"2009","journal-title":"Physiol. Meas."},{"key":"ref_24","first-page":"37","article-title":"A useful method for measuring daily physical activity by a three-direction monitor","volume":"29","author":"Sugimoto","year":"1997","journal-title":"Scand. J. Rehabil. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"S481","DOI":"10.1097\/00005768-200009001-00007","article-title":"The utility of the Digi-walker step counter to assess daily physical activity patterns","volume":"32","author":"Welk","year":"2000","journal-title":"Med. Sci. Sport. Exercise"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1097\/00005768-199907000-00020","article-title":"The prediction of speed and incline in outdoor running in humans using accelerometry","volume":"31","author":"Herren","year":"1999","journal-title":"Med. Sci. Sport. Exercise"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 Algorithms in Data Mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowledge and Information Systems"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huynh, T., and Schiele, B. (2006, January 11\u201314). Towards Less Supervision in Activity Recognition from Wearable Sensors. Montreux, Switzerland.","DOI":"10.1109\/ISWC.2006.286336"},{"key":"ref_30","unstructured":"Gjoreski, H., and Gams, M. Activity\/Posture Recognition Using Wearable Sensors Placed on Different Body Locations. 716\u2013724."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/7\/9183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:48:02Z","timestamp":1760219282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/7\/9183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7,17]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2013,7]]}},"alternative-id":["s130709183"],"URL":"https:\/\/doi.org\/10.3390\/s130709183","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,7,17]]}}}