Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers

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dc.contributor.author Fullerton, Elliot
dc.contributor.author Heller, Ben
dc.contributor.author Muñoz Organero, Mario
dc.date.accessioned 2021-01-26T12:39:26Z
dc.date.available 2021-01-26T12:39:26Z
dc.date.issued 2017-08-15
dc.identifier.bibliographicCitation E. Fullerton, B. Heller and M. Munoz-Organero, "Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers," in IEEE Sensors Journal, vol. 17, no. 16, pp. 5290-5297, 15 Aug.15, 2017
dc.identifier.issn 1530-437X
dc.identifier.uri http://hdl.handle.net/10016/31783
dc.description.abstract Recognizing human activity is very useful for an investigator about a patient's behavior and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity; however, research looking at the use of multiple body worn accelerometers in a free living environment to recognize a wide range of activities is not evident. This paper aimed to successfully recognize activity and sub-category activity types through the use of multiple body worn accelerometers in a free-living environment. Ten participants (Age = 23.1 +/- 1.7 years, height = 171.0 +/- 4.7 cm, and mass = 78.2 +/- 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and subcategory activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of preprocessing algorithms, feature, and classifier selections were tested, accuracy, and computing time were reported. A fine k-nearest neighbor classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities (> 95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Results show that recognition of activity and sub-category activity types is possible in a free-living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living.
dc.language.iso eng
dc.publisher IEEE
dc.rights ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.subject.other Human activity recognition
dc.subject.other Machine learning
dc.subject.other Body-worn accelerometers
dc.title Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers
dc.type research article
dc.identifier.doi https://doi.org/10.1109/JSEN.2017.2722105
dc.rights.accessRights open access
dc.identifier.publicationfirstpage 5290
dc.identifier.publicationissue 16
dc.identifier.publicationlastpage 5297
dc.identifier.publicationtitle IEEE Sensors Journal
dc.identifier.publicationvolume 17
dc.identifier.uxxi AR/0000020388
dc.affiliation.dpto UC3M. Departamento de Ingeniería Telemática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)
dc.type.hasVersion AM
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