XLearn : learning activity labels across heterogeneous datasets
Abstract
Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are used to classify human activities to support applications such as ambient assisted living, health monitoring, and behavioural intervention. Building a robust and meaningful classifier needs annotated ground truth, labelled with what activities are actually being observed—and acquiring high-quality, detailed, continuous annotations remains a challenging, time-consuming, and error-prone task, despite considerable attention in the literature. In this article, we use knowledge-driven ensemble learning to develop a technique that can combine classifiers built from individually labelled datasets, even when the labels are sparse and heterogeneous. The technique both relieves individual users of the burden of annotation and allows activities to be learned individually and then transferred to a general classifier. We evaluate our approach using four third-party, real-world smart home datasets and show that it enhances activity recognition accuracies even when given only a very small amount of training data.
Citation
Ye , J , Dobson , S A & Zambonelli , F 2020 , ' XLearn : learning activity labels across heterogeneous datasets ' , ACM Transactions on Intelligent Systems and Technology , vol. 11 , no. 2 , 17 . https://doi.org/10.1145/3368272
Publication
ACM Transactions on Intelligent Systems and Technology
Status
Peer reviewed
DOI
10.1145/3368272ISSN
2157-6904Type
Journal article
Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.