Gunthermann 2022 Adversarial Learning in Accelerometer based Transportation and Locomotion Mode Recognition.pdf (589.84 kB)
Adversarial learning in accelerometer based transportation and locomotion mode recognition
chapter
posted on 2023-06-10, 02:37 authored by Lukas Kornelius GunthermannLukas Kornelius Gunthermann, Lin Wang, Ivor SimpsonIvor Simpson, Andy PhilippidesAndy Philippides, Daniel RoggenDaniel RoggenThis chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically we address the problem of improving therecognition of human activities from smartphone sensors, when limited training data is available. Generative Adversarial Networks (GANs) provide an approach to model the distribution of a dataset and can be used to augment data to reduce the amount of labelled data required to train accurate classifiers. By introducing another fully connected neural network as classifier into a conditional GAN framework we utilise the adversarial learning approaches between discriminator and generator and between discriminator and classifier to perform semi–supervised learning on labelled and unlabelled samples. We evaluate our approach on the recognition of 8 modes of transportation and locomotion using the SHL dataset. This dataset is well established and has led to 3 public machine learning challenges, which allows to contrast our approach to the state of the art. Our GAN operates on 150 features extracted from 5s windows captured by a smartphone acceleration sensor carried at the hips. The most promising features are selected based on maximum relevance – minimum redundancy feature selection. We use Bayesian Search for hyperparameter optimisation. The resulting GAN classifier achieves 49% F1 score on a user independent evaluation, drastically outperforming our baseline at 35% F1 score.
History
Publication status
- Published
File Version
- Accepted version
Publisher
SpringerExternal DOI
Volume
217Page range
205-232Pages
355.0Book title
Generative adversarial learning: architectures and applicationsPlace of publication
ChamISBN
9783030913892Series
Intelligent Systems Reference LibraryDepartment affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Editors
Vasile Palade, Ariel Ruiz-Garcia, Roozbeh Razavi-Far, Juergen SchmidhuberLegacy Posted Date
2022-02-14First Compliant Deposit (FCD) Date
2022-02-14Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC