Augmentation of Segmented Motion Capture Data for Improving Generalization of Deep Neural Networks

Sawicki, Aleksander; Zieliński, Sławomir

doi:10.1007/978-3-030-47679-3_24

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In addition to SMOTE, there have been other proposals of interpolation for data augmentation. For example, Sawicki and Zielinski [69] used interpolation in combination with LSTMs on sensor data. In addition, an interpolation method similar to SMOTE was extended by DeVries and Taylor [70] to extrapolation by allowing λ in Eq.…”

Section: A Magnitude Domain Mixingmentioning

confidence: 99%

An empirical survey of data augmentation for time series classification with neural networks

2021

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In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

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Section: A Magnitude Domain Mixingmentioning

confidence: 99%

An empirical survey of data augmentation for time series classification with neural networks

2021

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“…The SMOTE has performed well in many similar time series applications such as wearable sensors [11] and electronic health records [12]. For timevariant datasets, the SMOTE was used in several variations, such as with deep learning [13], the weighted extreme learning machine [14], the furthest neighbour algorithm [15], cost minimisation [16], and the density-based SMOTE [17]. However, the SMOTE was not designed to perform on unlabelled datasets such as the one used in the applicative scenarios we considered.…”

Section: Literaturementioning

confidence: 99%

Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

et al. 2023

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With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.

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Application of LSTM Networks for Human Gait-Based Identification

Sawicki

Saeed

2021

Advances in Intelligent Systems and Computing

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Augmentation of Segmented Motion Capture Data for Improving Generalization of Deep Neural Networks

Cited by 5 publications

References 18 publications

An empirical survey of data augmentation for time series classification with neural networks

An empirical survey of data augmentation for time series classification with neural networks

Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

Application of LSTM Networks for Human Gait-Based Identification

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