Adversarial learning in accelerometer based transportation and locomotion mode recognition

Gunthermann, Lukas, Wang, Lin, Simpson, Ivor, Philippides, Andrew and Roggen, Daniel (2022) Adversarial learning in accelerometer based transportation and locomotion mode recognition. In: Razavi-Far, Roozbeh, Ruiz-Garcia, Ariel, Palade, Vasile and Schmidhuber, Juergen (eds.) Generative adversarial learning: architectures and applications. Intelligent Systems Reference Library, 217 . Springer, Cham, pp. 205-232. ISBN 9783030913892

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Abstract

This 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.

Item Type: Book Section
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 14 Feb 2022 12:25
Last Modified: 14 Feb 2022 12:27
URI: http://sro.sussex.ac.uk/id/eprint/104352

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