research-article

AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity Recognition

Authors:

Tobias Röddiger,

Michael BeiglAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 2

Article No.: 48, Pages 1 - 27

https://doi.org/10.1145/3659589

Published: 15 May 2024 Publication History

Abstract

Sensor-based HAR models face challenges in cross-subject generalization due to the complexities of data collection and annotation, impacting the size and representativeness of datasets. While data augmentation has been successfully employed in domains like natural language and image processing, its application in HAR remains underexplored. This study presents AutoAugHAR, an innovative two-stage gradient-based data augmentation optimization framework. AutoAugHAR is designed to take into account the unique attributes of candidate augmentation operations and the unique nature and challenges of HAR tasks. Notably, it optimizes the augmentation pipeline during HAR model training without substantially extending the training duration. In evaluations on eight inertial-measurement-units-based benchmark datasets using five HAR models, AutoAugHAR has demonstrated superior robustness and effectiveness compared to other leading data augmentation frameworks. A salient feature of AutoAugHAR is its model-agnostic design, allowing for its seamless integration with any HAR model without the need for structural modifications. Furthermore, we also demonstrate the generalizability and flexible extensibility of AutoAugHAR on four datasets from other adjacent domains. We strongly recommend its integration as a standard protocol in HAR model training and will release it as an open-source tool1.

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Cited By

R KS UV DR JR AA J(2024)Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G NetworksJournal of Machine and Computing10.53759/7669/jmc202404098(1058-1068)Online publication date: 5-Oct-2024
https://doi.org/10.53759/7669/jmc202404098
Choudhary SGowroju SKumar SSrinivas K(2024)Leveraging Neuromorphic Computing for Human Action Detection With Deep Neural NetworksRevolutionizing AI with Brain-Inspired Technology10.4018/979-8-3693-6303-4.ch018(429-458)Online publication date: 29-Nov-2024
https://doi.org/10.4018/979-8-3693-6303-4.ch018
Zhou HAyesh TFan CSarsenbayeva ZWithana A(2024)CoplayingVR: Understanding User Experience in Shared Control in Virtual RealityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785088:3(1-25)Online publication date: 9-Sep-2024
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Show More Cited By

Index Terms

AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity Recognition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Human-centered computing
  1. Human computer interaction (HCI)
  2. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 2

June 2024

1330 pages

EISSN:2474-9567

DOI:10.1145/3665317

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Published: 15 May 2024

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Cited By

R KS UV DR JR AA J(2024)Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G NetworksJournal of Machine and Computing10.53759/7669/jmc202404098(1058-1068)Online publication date: 5-Oct-2024
https://doi.org/10.53759/7669/jmc202404098
Choudhary SGowroju SKumar SSrinivas K(2024)Leveraging Neuromorphic Computing for Human Action Detection With Deep Neural NetworksRevolutionizing AI with Brain-Inspired Technology10.4018/979-8-3693-6303-4.ch018(429-458)Online publication date: 29-Nov-2024
https://doi.org/10.4018/979-8-3693-6303-4.ch018
Zhou HAyesh TFan CSarsenbayeva ZWithana A(2024)CoplayingVR: Understanding User Experience in Shared Control in Virtual RealityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785088:3(1-25)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678508
Salunkhe SShinde SPatil PBurchard RKostakos VKay JHoang T(2024)Augmentation Appproaches to Refine Wearable Human Activity RecognitionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678464(603-606)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678464
Zhou HSomarathne PPeirispulle TFan CSarsenbayeva ZWithana A(2024)PairPlayVR: Shared Hand Control for Virtual GamesProceedings of the Augmented Humans International Conference 202410.1145/3652920.3653057(311-314)Online publication date: 4-Apr-2024
https://dl.acm.org/doi/10.1145/3652920.3653057
Liaquat MNugent CCleland IKhan N(2024)Balancing Real and Synthetic Data for Enhanced Human Activity Recognition: An Empirical StudyProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)10.1007/978-3-031-77571-0_20(194-204)Online publication date: 21-Dec-2024
https://doi.org/10.1007/978-3-031-77571-0_20
Huang YZhou YZhao HFang LRiedel TBeigl M(2024)ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series ModelsMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_27(429-446)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70381-2_27

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