Recent trends in machine learning for human activity recognition—A survey
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2018
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Citation of Original Publication
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
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This 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.
Abstract
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.