Lifelong Learning in Sensor-Based Human Activity Recognition

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. Driv...

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Bibliographic Details
Published inIEEE pervasive computing Vol. 18; no. 3; pp. 49 - 58
Main Authors Ye, Juan, Dobson, Simon, Zambonelli, Franco
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Activity recognition
Adaptation models
Artificial intelligence
Data models
Human activity recognition
Lifelong learning
Machine learning
Sensor phenomena and characterization
Task analysis
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Summary: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.
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ISSN:1536-1268
1558-2590
DOI:10.1109/MPRV.2019.2913933