@article{abdallah_activity_2018,
	title = {Activity {Recognition} with {Evolving} {Data} {Streams}: {A} {Review}},
	volume = {51},
	issn = {0360-0300},
	shorttitle = {Activity {Recognition} with {Evolving} {Data} {Streams}},
	url = {https://doi.org/10.1145/3158645},
	doi = {10.1145/3158645},
	abstract = {Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This article surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this article is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed, and the possible directions of future research are indicated.},
	number = {4},
	urldate = {2022-03-15},
	journal = {ACM Computing Surveys},
	author = {Abdallah, Zahraa S. and Gaber, Mohamed Medhat and Srinivasan, Bala and Krishnaswamy, Shonali},
	month = jul,
	year = {2018},
	keywords = {Activity recognition, adaptive learning, stream mining, transfer learning},
	pages = {71:1--71:36},
}

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