Activity Recognition with Evolving Data Streams: A Review

Abdallah, Zahraa S. and Gaber, Mohamed Medhat and Srinivasan, Bala and Krishnaswamy, Shonali (2018) Activity Recognition with Evolving Data Streams: A Review. ACM Computing Surveys, 51 (4). pp. 1-36. ISSN 0360-0300

Preview

Text Activity Recognition with Evolving Data Streams.pdf - Accepted Version Download (898kB)

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 paper surveys the two overlapped areas of research of activity recognition and data
stream mining. The perspective of this paper 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.

Item Type:	Article
Identification Number:	10.1145/3158645
Dates:	Date Event 8 November 2017 Accepted July 2018 Published
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Ian Mcdonald
Date Deposited:	13 Nov 2017 12:57
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/5303

Actions (login required)

View Item