Improving Cross-Subject Activity Recognition via Adversarial Learning
Loading...
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Author
Date
2020-05-11
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
90542-90554
90542-90554
Series
IEEE Access, Volume 8
Abstract
Deep learning has been widely used for implementing human activity recognition from wearable sensors like inertial measurement units. The performance of deep activity recognition is heavily affected by the amount and variability of the labeled data available for training the deep learning models. On the other hand, it is costly and time-consuming to collect and label data. Given limited training data, it is hard to maintain high performance across a wide range of subjects, due to the differences in the underlying data distribution of the training and the testing sets. In this work, we develop a novel solution that applies adversarial learning to improve cross-subject performance by generating training data that mimic artificial subjects - i.e. through data augmentation - and enforcing the activity classifier to ignore subject-dependent information. Contrary to domain adaptation methods, our solution does not utilize any data from subjects of the test set (or target domain). Furthermore, our solution is versatile as it can be utilized together with any deep neural network as the classifier. Considering the open dataset PAMAP2, nearly 10% higher cross-subject performance in terms of F1-score can be achieved when training a CNN-LSTM-based classifier with our solution. A performance gain of 5% is also observed when our solution is applied to a state-of-the-art HAR classifier composed of a combination of inception neural network and recurrent neural network. We also investigate different influencing factors of classification performance (i.e. selection of sensor modalities, sampling rates and the number of subjects in the training data), and summarize a practical guideline for implementing deep learning solutions for sensor-based human activity recognition.Description
| openaire: EC/H2020/777222/EU//ATTRACT
Keywords
Human activity recognition, deep learning, adversarial learning, data augmentation, cross-subject performance
Citation
Souza Leite , C & Xiao , Y 2020 , ' Improving Cross-Subject Activity Recognition via Adversarial Learning ' , IEEE Access , vol. 8 , 9091200 , pp. 90542-90554 . https://doi.org/10.1109/ACCESS.2020.2993818
