Ramamurthy, Sreenivasan RamasamyRoy, Nirmalya2018-04-192018-04-192018Ramasamy 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.1254http://hdl.handle.net/11603/8781"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."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.16 pagesen-USThis 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.active learningactivity recognitiondata miningdeep learningmachine learningtransfer learningwearable sensorsUMBC Mobile Pervasive & Sensor Computing LabText