Time Series Segmentation Using Neural Networks with Cross-Domain Transfer Learning

Matias, Pedro; Folgado, Duarte; Gamboa, Hugo; Carreiro, André V.

doi:10.3390/electronics10151805

Cited by 16 publications

(12 citation statements)

References 56 publications

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“…All basic DL modules were stacked three times, including batch normalization and ReLu activation function; finally, an output having the same feature size as the input data was obtained through a fully connected layer. In addition, several models presented in [ 25 , 26 , 27 , 28 , 29 ] that reported high accuracy by applying the CEF method to ECG data were adopted for comparison. As mentioned in Section 2 , the loss is calculated by comparing the output of all models with the same size as the input and the label on the time axis.…”

Section: Resultsmentioning

confidence: 99%

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition

Lee

Kim

2023

Sensors

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With the development of wearable devices such as smartwatches, several studies have been conducted on the recognition of various human activities. Various types of data are used, e.g., acceleration data collected using an inertial measurement unit sensor. Most scholars segmented the entire timeseries data with a fixed window size before performing recognition. However, this approach has limitations in performance because the execution time of the human activity is usually unknown. Therefore, there have been many attempts to solve this problem through the method of activity recognition by sliding the classification window along the time axis. In this study, we propose a method for classifying all frames rather than a window-based recognition method. For implementation, features extracted using multiple convolutional neural networks with different kernel sizes were fused and used. In addition, similar to the convolutional block attention module, an attention layer to each channel and spatial level is applied to improve the model recognition performance. To verify the performance of the proposed model and prove the effectiveness of the proposed method on human activity recognition, evaluation experiments were performed. For comparison, models using various basic deep learning modules and models, in which all frames were classified for recognizing a specific wave in electrocardiography data were applied. As a result, the proposed model reported the best F1-score (over 0.9) for all kinds of target activities compared to other deep learning-based recognition models. Further, for the improvement verification of the proposed CEF method, the proposed method was compared with three types of SW method. As a result, the proposed method reported the 0.154 higher F1-score than SW. In the case of the designed model, the F1-score was higher as much as 0.184.

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Section: Resultsmentioning

confidence: 99%

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition

Lee

Kim

2023

Sensors

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“…There is close to no research on applying TL approaches to TSS settings and on analyzing what factors increase or limit the benefits of TL. One of the very few contribution covering the topic showed promising results when applying cross-domain TL to boost medical TSS, even though the scope of the analysis concerning TL is very limited [9]. In general, no authors have ever performed a study focusing on how TL can be used to boost the performance of deep learning TSS models.…”

Section: Of 16mentioning

confidence: 99%

“…While TSS problems might be less prevalent in everyday life of companies and researchers, they still deserve attention due to their importance for fields like human action recognition [26], sleep staging [5], and operational state detection [6]. One contribution had a first look at the topic [9] and successfully showed that feature extraction process of a TSS-CNN model can be improved when pretraining the model with a large, domain-independent source dataset. However, a broad analysis focusing on TL as well as a detailed discussion of the implications of the results was not performed.…”

Section: Transfer Learning For Time Seriesmentioning

confidence: 99%

Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles

Gaugel¹,

Reichert²

2023

Preprint

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Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

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“…Emerging works focus specifically on biosignal segmentation, e.g., applying neural networks (NN) to ECG signals. In [ 61 ], an NN with transfer learning was used for the segmentation of periodic biosignals (motion and ECG). Convolutional NN has also been found for ECG segmentation.…”

Section: Related Literaturementioning

confidence: 99%

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

et al. 2022

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Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

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Time Series Segmentation Using Neural Networks with Cross-Domain Transfer Learning

Cited by 16 publications

References 56 publications

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition

Industrial Transfer Learning for Multivariate Time Series Segmentation: A Case Study on Hydraulic Pump Testing Cycles

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

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