Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Jiang, Zhinong; Lai, Yuehua; Zhang, Jinjie; Zhao, Haipeng; Mao, Zhiwei

doi:10.3390/s19245488

Cited by 18 publications

(11 citation statements)

References 20 publications

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“…In this study, 1D-CNN and GRU ensemble classification models were introduced for the BBS scoring algorithm. The 1D-CNN and LSTM models often show good performance on multivariate time-series data [39][40][41]. Because the amount of BBS data is small, each 1D-CNN and GRU model was constructed with a shallow structure, which is advantageous for small amounts of data [42,43].…”

Section: Classification Modelmentioning

confidence: 99%

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Kim

Kyunglim

Jeong

et al. 2021

Sensors

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In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

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Section: Classification Modelmentioning

confidence: 99%

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Kim

Kyunglim

Jeong

et al. 2021

Sensors

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“…Poma et al investigated optimization of CNNs using Fuzzy Gravitational Search Algorithm method (FGSA) for pattern recognition and image classification (Poma, Melin, González, & Martinez, 2020; Poma, Melin, González, & Martínez, 2020). However, it has been proven that 1D-CNN can be applied efficiently for time series signals analysis (Jiang et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Features were extracted by the 1D-Convolution Network from each segment of force-plate data using the following equation: In equation (17), f ij is the extracted features vector from the jth neuron of the ith filter in the hidden layer, φ is the activation function which was assigned as “RELU” in this work, b i is the i-th filter corresponding overall bias, w ik the featuring weight matrix, and x j+k-1 represents the input signals vector (Jiang et al, 2019). “RELU” provides the non-linear transformation of the input data for a better hypothesis space generated from its deeper representation.…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Networks for Human’s Fall-risk Prediction using Force-Plate Time Series Signal

Savadkoohi

Oladunni

Thompson

2021

Preprint

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Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks' capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18-86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human's fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.

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“…The LSTM algorithm is a special kind of recurrent neural network (RNNs) that manages sequential information by memorizing the information for long periods [25,26]. Unlike traditional RNNs, LSTM adds a new framework called a "memory cell" with an internal state to store valuable information [27].…”

Section: Long Short-term Memory (Lstm) Algorithmmentioning

confidence: 99%

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Manie

Peng

et al. 2020

Sensors

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In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

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Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Cited by 18 publications

References 20 publications

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Deep Neural Networks for Human’s Fall-risk Prediction using Force-Plate Time Series Signal

Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

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