Lifelong learning in sensor-based human activity recognition

Juan Ye*, Simon Andrew Dobson*, Franco Zambonelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Human activity recognition (HAR) systems will be increasingly deployed in real-world environments and for long periods of time. This significantly challenges current approaches to HAR, which have to account for changes in activity routines, the evolution of situations, and sensing technologies. Driven by these challenges, in this paper, we argue the need to move beyond learning to lifelong machine learning—with the ability to incrementally and continuously adapt to changes in the environment being learned. We introduce a conceptual framework for lifelong machine learning to structure various relevant proposals in the area and identify some key research challenges that remain.
Original languageEnglish
Pages (from-to)49-58
Number of pages10
JournalIEEE Pervasive Computing
Volume18
Issue number3
DOIs
Publication statusPublished - 18 Nov 2019

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