Lifelong learning in sensor-based human activity recognition
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
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
IEEE Pervasive Computing
Copyright © 2019 IEEE Computer Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/MPRV.2019.2913933
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.