Recent trends in machine learning for human activity recognition—A survey
Links to Fileshttps://doi.org/10.1002/widm.1254
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Type of Work16 pages
Citation of Original PublicationRamasamy 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
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UMBC Mobile Pervasive & Sensor Computing Lab
There 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.
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