Title
Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments.
Abstract
In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.
Year
DOI
Venue
2020
10.3390/s20010216
SENSORS
Keywords
DocType
Volume
human activity recognition,neural networks,ensemble neural networks,model conflict resolution,smart environments
Journal
20
Issue
ISSN
Citations 
1
1424-8220
1
PageRank 
References 
Authors
0.35
29
5
Name
Order
Citations
PageRank
Naomi Irvine110.35
Chris D. Nugent21150128.39
Shuai Zhang3439.10
hui wang47617.01
Wing W. Y. Ng552856.12