Convolutional neural networks for time series classification

Zhao, Bo; Lü, Huanzhang; Chen, Shangfeng; Liu, Junliang; Wu, Dongya

doi:10.21629/jsee.2017.01.18

Cited by 577 publications

(227 citation statements)

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“…Similarly, time series can exhibit locally correlated points that are invariant in translation. The performance of deep CNNs for the classification of unidimensional or multidimensional time series is attested [21,22]. Like for image classification, CNNs can extract deep features from a signal's internal structure.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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Fig. 6. CAM analysis. Twenty-four segments were randomly selected from the training set containing the gait data of six participants (left columns). These segments were used to fine-tune the last layers of the convolutional neural network (CNN) that was pre-trained on the training set of the 30 other participants. This CNN classified 30 segments drawn randomly from the test set with 100% accuracy (right columns). Class activation mapping (CAM) was performed on each sample of the test set. Color coding shows which parts of the signals are prioritized to be used by the CNN to perform classification. Warm color (red, orange): high focus; cold colors (green, blue): low focus. AbstractThe fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles . A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits.

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Section: Introductionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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“…It is also clear from the table that the use of ESN-RAE to create new data code give better results than directly squirting the original data into SVM classifier. Method CA STMF [52] 0.700 SVM [62] 0.790 Swale [41] 0.830 SpADe [41] 0.744 GeTeM [41] 0.800 1-NN [62] 0.880 CNN [62] 0.980 Jeong's method [62] 0.840 Gorecki's method [62] 0.830 LS [59] 0.870 FS [59] 0.766 ST+FCBF [59] 0.766 LPP [45] 0.710 NCC [52] 0.770 N5S2 [56] 0.770 EDTW [45] 0.825 N8S5 [56] 0.840 DSVM [45] 0.855 SVM 0.816 ESN-RAE 0.846 ML-ESN-RAE 0.877…”

Section: 1mentioning

confidence: 99%

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

et al. 2019

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“…It consists of a traditional CNN, where each convolutional layer is followed by a max pooling operation, then a traditional fully connected layer is used before the final softmax classifier. 6) Time Convolutional Neural Network: (Time-CNN) was originally proposed for univariate as well as multivariate TSC [25]. Similarly to MCDCNN, this network is a traditional CNN with one major exception: the use of the mean squared error instead of the traditional categorical cross-entropy loss function, which has been used by all the deep learning approaches we have mentioned so far.…”

Section: A Architecturesmentioning

confidence: 99%

Deep Neural Network Ensembles for Time Series Classification

Fawaz

Forestier

Weber

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.

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Convolutional neural networks for time series classification

Cited by 577 publications

References 0 publications

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

Deep Neural Network Ensembles for Time Series Classification

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