Model training through synthetic data generation:Investigating the impact on human physical fatigue

Lambay, Arsalan Jaweed, Morgan, Phillip L. ORCID: https://orcid.org/0000-0002-5672-0758, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2024. Model training through synthetic data generation:Investigating the impact on human physical fatigue. Presented at: International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24 - 27 July 2024. Published in: Nunes, Isabel L. ed. AHFE International: Human Factors and Systems Interaction. , vol.154 New York: AHFE Open Access, pp. 22-31. 10.54941/ahfe1005349

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Abstract

Collaborative robots, or cobots, are one of the Industry 4.0 technologies that have and continue to change many industrial procedures. However, amid this technological advancement, the persisting physical strain on human workers remains a significant concern. Even with the advent of cobots aimed at alleviating burdensome tasks, certain physical jobs continue to induce fatigue in human workers. Addressing this challenge necessitates the development of robust solutions that combine technological innovation with human-centric considerations. One critical aspect in mitigating physical fatigue in human workers involves the application of Machine Learning (ML) models. These models heavily depend on data obtained from real-world situations that accurately represent the complexities of physical strain. However, this kind of data is frequently limited and costly to gather using sensors, which hinders the development of an effective ML model. This scarcity underscores the need for alternative approaches, with Synthetic Data Generation (SDG) emerging as a viable solution to this problem. The production of synthetic data offers a new approach to address the lack of relevant data needed to train machine learning algorithms. By employing techniques like Tabular Generative Adversarial Networks (GANs), synthetic datasets can be created, simulating realistic human physical fatigue detection features. Tabular GANs have, for example, been shown to be effective in creating synthetic data that closely resembles the statistical characteristics and patterns of real-world datasets. Furthermore, tabular GANs present a scalable and affordable response to the problem of data scarcity. The research reported here presents a novel approach centred on employing the Tabular GAN methodology to create synthetic datasets encompassing key features pertinent to the detection of human physical fatigue. The results of this study are expected to contribute substantially to creating robust solutions to alleviate physical strain and enhance human workers' overall well-being in industrial settings. The goal is to create datasets that accurately represent the complexities found in real-world scenarios where physical fatigue notably influences human performance. These synthetically generated datasets will serve as the foundation for training specialized ML models designed explicitly for detecting the development of human physical fatigue. The trained ML model will undergo rigorous testing and validation using a substantial repository of authentic real-world data. The model's accuracy and reliability in detecting human physical fatigue will be assessed through this evaluation process. The ultimate objective is to achieve a level of accuracy that demonstrates the model's proficiency in identifying and predicting the onset of physical fatigue in human workers within industrial settings. This research endeavours to bridge the gap between Industry 4.0 innovations and human well-being by leveraging synthetic data generation techniques to enhance the accuracy and efficiency of ML models in detecting human physical fatigue.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Engineering Psychology
Subjects:	T Technology > TJ Mechanical engineering and machinery
Publisher:	AHFE Open Access
ISBN:	9781964867304
Date of First Compliant Deposit:	28 May 2024
Date of Acceptance:	May 2024
Last Modified:	23 Jul 2024 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/169226

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