ORCID Profile
0000-0002-9126-2107
Current Organisation
University of Adelaide
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Publisher: IEEE
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 03-2021
Publisher: IEEE
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 28-10-2022
Publisher: Elsevier BV
Date: 10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2019
Publisher: ACM
Date: 09-08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Elsevier BV
Date: 12-2020
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: No publisher found
Date: 2018
Publisher: IEEE
Date: 06-2022
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 10-2017
Publisher: arXiv
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer International Publishing
Date: 2018
Publisher: arXiv
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 14-10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.
Publisher: No publisher found
Date: 2012
Publisher: Elsevier BV
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: IEEE
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Association for Computing Machinery (ACM)
Date: 19-03-2021
DOI: 10.1145/3448083
Abstract: Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis. Although our collective know-how to solve Human Activity Recognition (HAR) problems with wearables has progressed immensely with end-to-end deep learning paradigms, several fundamental opportunities remain overlooked. We rigorously explore these new opportunities to learn enriched and highly discriminating activity representations. We propose: i) learning to exploit the latent relationships between multi-channel sensor modalities and specific activities ii) investigating the effectiveness of data-agnostic augmentation for multi-modal sensor data streams to regularize deep HAR models and iii) incorporating a classification loss criterion to encourage minimal intra-class representation differences whilst maximising inter-class differences to achieve more discriminative features. Our contributions achieves new state-of-the-art performance on four erse activity recognition problem benchmarks with large margins---with up to 6% relative margin improvement. We extensively validate the contributions from our design concepts through extensive experiments, including activity misalignment measures, ablation studies and insights shared through both quantitative and qualitative studies. The code base and trained network parameters are open-sourced on GitHub github.com/AdelaideAuto-IDLab/Attend-And-Discriminate to support further research.
Publisher: No publisher found
Date: 2016
Publisher: IEEE
Date: 06-2022
Publisher: No publisher found
Date: 2013
Publisher: IEEE
Date: 10-2021
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: IEEE
Date: 06-2010
Publisher: No publisher found
Date: 2019
Publisher: ACM
Date: 24-10-2016
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Springer Science and Business Media LLC
Date: 19-08-2020
Publisher: IEEE
Date: 06-2016
Publisher: ACM
Date: 05-11-2018
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 06-2020
Publisher: ACM
Date: 04-09-2020
Publisher: Springer Science and Business Media LLC
Date: 21-03-2018
Publisher: IEEE
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: IEEE
Date: 06-2016
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 2022
Publisher: IEEE
Date: 06-2022
Publisher: IEEE
Date: 06-2013
Publisher: Springer International Publishing
Date: 2020
Publisher: No publisher found
Date: 2018
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 2021
Publisher: Elsevier BV
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Elsevier BV
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 2019
Publisher: Elsevier BV
Date: 07-2012
Publisher: IEEE
Date: 06-2022
Publisher: Elsevier BV
Date: 03-2019
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 10-2021
Publisher: Elsevier BV
Date: 09-2017
Publisher: No publisher found
Date: 2020
Start Date: 2021
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 2016
Funder: Australian Research Council
View Funded Activity