Lifelong learning in sensor-based human activity recognition
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.
Citation
Ye , J , Dobson , S A & Zambonelli , F 2019 , ' Lifelong learning in sensor-based human activity recognition ' , IEEE Pervasive Computing , vol. 18 , no. 3 , pp. 49-58 . https://doi.org/10.1109/MPRV.2019.2913933
Publication
IEEE Pervasive Computing
Status
Peer reviewed
ISSN
1536-1268Type
Journal article
Collections
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