Data Augmentation with Suboptimal Warping for Time-Series Classification

Kamycki, Krzysztof; Kapuściński, Tomasz; Oszust, Mariusz

doi:10.3390/s20010098

Cited by 45 publications

(20 citation statements)

References 26 publications

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“…The data augmentation technique has an advantage of simulating a real-world training dataset by transforming raw data. It can reduce labor-work for gathering a large number of datasets of falls [38]. Additionally, the synthetic training data by applying data augmentation technique can minimize over-fitting effects, increase generality of unseen data and remove bias from the trained model due to imbalance volume of training data.…”

Section: Discussionmentioning

confidence: 99%

“…Data augmentation techniques are widely utilized in computer vision to introduce new data samples between pairs of training datasets. Classification with time-series data with a small number of samples may lead to an overfitting problem [38]. To overcome this limitation, time-series data augmentation technique is applied in this study.…”

Section: Data Augmentationmentioning

confidence: 99%

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Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Kim

Choi

Heo

et al. 2020

Sensors

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Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

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Section: Discussionmentioning

confidence: 99%

Section: Data Augmentationmentioning

confidence: 99%

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Kim

Choi

Heo

et al. 2020

Sensors

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“…For image data, the augmentation process involves different image manipulation techniques, such as rotation, translation, scaling, and flipping arrangements [81]. The challenging part for data augmentation are memory and computational constraints [82]. There are two popular data augmentation methods: online and offline data augmentation [83].…”

Section: Pre-processingmentioning

confidence: 99%

“…Online data augmentation is carried out on the fly during training, whereas offline data augmentation produces data in advance and stores it in memory [83]. The online approach saves storage but results in a longer training time, whereas the offline approach is faster in terms of training, although it consumes a large amount of memory [80][81][82][83].…”

Section: Pre-processingmentioning

confidence: 99%

Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

Ayana

Dese

Choe

2021

Cancers

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Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges—as well as outlooks—are discussed.

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“…Their results showed that additive noise and sliding windows provided the highest accuracy increase across different tasks. Furthermore, the authors of [ 12 ] have developed a method based on sub-optimally aligned sequences for generating augmented data. Their approach evaluated across different datasets, including EEG and ECG signals, yielded better or equivalent performance compared to existing methods.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation of Surface Electromyography for Hand Gesture Recognition

Tsinganos

Cornelis

et al. 2020

Sensors

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The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

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Data Augmentation with Suboptimal Warping for Time-Series Classification

Cited by 45 publications

References 26 publications

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

Data Augmentation of Surface Electromyography for Hand Gesture Recognition

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