Physical activity recognition from smartphone accelerometer data for user context awareness sensing

dc.contributor.authorWannenburg, Johann
dc.contributor.authorMalekian, Reza
dc.date.accessioned2018-04-09T05:11:44Z
dc.date.available2018-04-09T05:11:44Z
dc.date.issued2017-12
dc.description.abstractPhysical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.librarianhj2018en_ZA
dc.description.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021en_ZA
dc.identifier.citationWannenburg, J. & Malekian, R. 2017, 'Physical activity recognition from smartphone accelerometer data for user context awareness sensing', IEEE Transactions on Systems, Man, and Cybernetics : Systems, vol. 47, no. 12, pp. 3 142-3149.en_ZA
dc.identifier.issn2168-2216 (print)
dc.identifier.issn2168-2232 (online)
dc.identifier.other10.1109/TSMC.2016.2562509
dc.identifier.urihttp://hdl.handle.net/2263/64425
dc.language.isoenen_ZA
dc.publisherInstitute of Electrical and Electronics Engineersen_ZA
dc.rights© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en_ZA
dc.subjectAccelerometeren_ZA
dc.subjectActivity recognitionen_ZA
dc.subjectMachine learningen_ZA
dc.subjectMachine learning algorithmsen_ZA
dc.subjectFeature extractionen_ZA
dc.subjectHidden Markov modelsen_ZA
dc.subjectSensorsen_ZA
dc.subjectUbiquitous computingen_ZA
dc.subjectPattern classificationen_ZA
dc.subjectLearning (artificial intelligence)en_ZA
dc.subjectSmart phoneen_ZA
dc.titlePhysical activity recognition from smartphone accelerometer data for user context awareness sensingen_ZA
dc.typePostprint Articleen_ZA

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