Recent trends in machine learning for human activity recognition—A survey

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dc.contributor.authorRamamurthy, Sreenivasan Ramasamy
dc.contributor.authorRoy, Nirmalya
dc.date.accessioned2018-04-19T18:18:32Z
dc.date.available2018-04-19T18:18:32Z
dc.date.issued2018
dc.description"This is the pre-peer reviewed version of the following article: Ramasamy Ramamurthy S, Roy N. Recent trends in machine learning for human activity recognition—A survey. WIREs Data Mining Knowl Discov. 2018;e1254. https://doi.org/10.1002/widm.1254, which has been published in final form at https://doi.org/10.1002/widm.1254. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."
dc.description.abstractThere has been an upsurge recently in investigating machine learning techniques for Activity Recognition (AR) problems as that have been very effective in extracting and learn-ing knowledge from the activity datasets. The techniques ranges from heuristically derived hand-crafted feature-based traditional machine learning algorithms to the recently de-veloped hierarchically self-evolving feature-based deep learn-ing algorithms. AR continues to remain a challenging prob-lem in uncontrolled smart environments despite the amount of work contributed by the researcher in this field. The com-plex, volatile, and chaotic nature of the activity data presents numerous challenges which influence the performance of the AR systems in the wild. In this article, we present a com-prehensive overview of recent machine learning and data mining techniques generally employed for AR and the under-pinning problems and challenges associated with existing systems. We also articulate the recent advances and state-of-the-art techniques in this domain in an attempt to iden-tify the possible directions for future activity recognition research.en_US
dc.description.sponsorshipThis work is partially supported by the Office of Naval Research Grant N00014-15-1-2229 and the Alzheimer’s Association Research Grant AARG-17-533039.en_US
dc.description.urihttps://doi.org/10.1002/widm.1254en_US
dc.format.extent16 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/M29882Q0G
dc.identifier.citationRamasamy Ramamurthy S, Roy N. Recent trends in machine learning for human activity recognition—A survey. WIREs Data Mining Knowl Discov. 2018;e1254. https://doi.org/10.1002/widm.1254en_US
dc.identifier.urihttp://hdl.handle.net/11603/8781
dc.language.isoen_USen_US
dc.publisherWileyen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectactive learningen_US
dc.subjectactivity recognitionen_US
dc.subjectdata miningen_US
dc.subjectdeep learningen_US
dc.subjectmachine learningen_US
dc.subjecttransfer learningen_US
dc.subjectwearable sensorsen_US
dc.subjectUMBC Mobile Pervasive & Sensor Computing Lab
dc.titleRecent trends in machine learning for human activity recognition—A surveyen_US
dc.title.alternativeRecent Machine Learning Trends in Human Activity Recognition--A survey
dc.typeTexten_US

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