Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation. To that end, our goal is to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs. Deep Neural Networks (DNNs), and in particular Convolutional Neural Networks (CNNs), have been gaining traction in the field of stress analysis, exhibiting promising results. Furthermore, the electrocardiogram (ECG) signals, have also been widely adopted for estimating levels of stress. In this work, we propose two CNN architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw ECG signals (3 s). One architecture is simple and with a low memory footprint, suitable for running in wearable edge-computing nodes, and the other is able to learn more complex features, having more trainable parameters. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.

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Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Solar Energy ◽

10.1016/j.solener.2021.05.095 ◽

2021 ◽

Vol 224 ◽

pp. 341-354

Author(s):

Yuhao Nie ◽

Ahmed S. Zamzam ◽

Adam Brandt

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Short Term

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Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

Journal of Scientific Research ◽

10.3329/jsr.v13i3.52332 ◽

2021 ◽

Vol 13 (3) ◽

pp. 809-820

Author(s):

V. Sowmya ◽

R. Radha

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Traffic Management ◽

Data Augmentation ◽

Vehicle Detection ◽

Heavy Vehicles ◽

Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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Real-Time Prediction of Anode Potential in Li-Ion Batteries Using Long Short-Term Neural Networks for Lithium Plating Prevention

Journal of The Electrochemical Society ◽

10.1149/2.0621910jes ◽

2019 ◽

Vol 166 (10) ◽

pp. A1893-A1904 ◽

Cited By ~ 2

Author(s):

Xianke Lin

Keyword(s):

Neural Networks ◽

Real Time ◽

Anode Potential ◽

Li Ion Batteries ◽

Short Term ◽

Time Prediction ◽

Li Ion

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A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

Applied Sciences ◽

10.3390/app10196755 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6755

Author(s):

Carlos Iturrino Garcia ◽

Francesco Grasso ◽

Antonio Luchetta ◽

Maria Cristina Piccirilli ◽

Libero Paolucci ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Validation Dataset ◽

Short Term ◽

Current Time ◽

Term Memory ◽

Long Short Term Memory

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

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Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

Biosensors ◽

10.3390/bios11060188 ◽

2021 ◽

Vol 11 (6) ◽

pp. 188

Author(s):

Li-Ren Yeh ◽

Wei-Chin Chen ◽

Hua-Yan Chan ◽

Nan-Han Lu ◽

Chi-Yuan Wang ◽

...

Keyword(s):

Neural Networks ◽

Real Time ◽

Deep Neural Networks ◽

Network Models ◽

Kappa Statistics ◽

Neural Network Models ◽

Ecg Signals ◽

St Depression ◽

Deep Model ◽

St Elevation

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

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Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences ◽

10.1109/iecbes.2012.6498165 ◽

2012 ◽

Cited By ~ 4

Author(s):

Vishnu P. Nambiar ◽

Mohamed Khalil-Hani ◽

M. N. Marsono

Keyword(s):

Neural Networks ◽

Real Time ◽

Ecg Signals ◽

Heart Arrhythmia ◽

Block Based ◽

Real Time Classification

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The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp40776.2020.9053800 ◽

2020 ◽

Author(s):

Faezeh Nejati Hatamian ◽

Nishant Ravikumar ◽

Sulaiman Vesal ◽

Felix P. Kemeth ◽

Matthias Struck ◽

...

Keyword(s):

Atrial Fibrillation ◽

Neural Networks ◽

Deep Neural Networks ◽

Data Augmentation ◽

Ecg Signals

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Short-Term Ageing Influence on the Creep and Recovery Characteristics of Asphalt Binders

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.275-277.2045 ◽

2013 ◽

Vol 275-277 ◽

pp. 2045-2050 ◽

Cited By ~ 1

Author(s):

Chao Wang ◽

Jin Xi Zhang ◽

Ping Ping Song

Keyword(s):

Stress Level ◽

High Stress ◽

Asphalt Binders ◽

Short Term ◽

Modified Asphalt ◽

Creep And Recovery ◽

Modified Binder ◽

High Stress Level ◽

Almost All ◽

Sbs Modified Asphalt

This paper focused on the short-term ageing influence on the creep and recovery characteristics of neat and SBS modified asphalt binders at two different stress levels. Using the MSCR test conducted on dynamic shear rheometer (DSR), it can be concluded that the RTFOT short-term ageing process was observed to decrease the nonrecoverable compliance (Jnr) and increase the average recovery percent (R) for almost all binders, which was more notable at a high stress level especially for SBS modified binders. Additionally, stress level applied in test played a key role in evaluating the viscoelastic properties of different asphalt binders, and SBS modified binder exhibited more stress sensitive than neat binders.

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