A novel method to monitor human stress states using ultra-short-term ECG spectral feature

2017 ◽  
Author(s):  
Bosun Hwang ◽  
Ji Woo Ryu ◽  
Cheolsoo Park ◽  
Byoung-Tak Zhang
Keyword(s):  
Spectral Feature ◽  
Short Term ◽  
Human Stress ◽  
Novel Method ◽  
Stress States

Bee Markers: A Novel Method for Non-Invasive Short Term Marking of Bats

2020 ◽  
Vol 21 (2) ◽  
pp. 465 ◽  
Author(s):  
Lucinda Kirkpatrick ◽  
Grzegorz Apoznański ◽  
Luc De Bruyn ◽  
Ralf Gyselings ◽  
Tomasz Kokurewicz
Keyword(s):  
Short Term ◽  
Non Invasive ◽  
Novel Method

scholarly journals Maturity, Indebtedness, and Default Risk

2012 ◽  
Vol 102 (6) ◽  
pp. 2674-2699 ◽  
Author(s):  
Satyajit Chatterjee ◽  
Burcu Eyigungor
Keyword(s):  
Standard Deviation ◽  
Default Risk ◽  
Sovereign Debt ◽  
External Debt ◽  
Equilibrium Price ◽  
Short Term ◽  
Price Function ◽  
Output Ratio ◽  
Novel Method

We advance quantitative-theoretic models of sovereign debt by proving the existence of a downward sloping equilibrium price function for long-term debt and implementing a novel method to accurately compute it. We show that incorporating long-term debt allows the model to match Argentina's average external debt-to-output ratio, average spread on external debt, the standard deviation of spreads, and simultaneously improve upon the model's ability to account for Argentina's other cyclical facts. We also investigated the welfare properties of maturity length and showed that if the possibility of self-fulfilling rollover crises is taken into account, long-term debt is superior to short-term debt. (JEL E23, E32, F34, O11, O19)

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Prerequisites of human stress states diagnostics with the use of THz radiation

2018 ◽  
Author(s):  
E. E. Berlovskaya ◽  
A. S. Sinko ◽  
I. A. Ozheredov ◽  
T.V. Adamovich ◽  
E. S. Isaychev ◽  
...  
Keyword(s):  
Thz Radiation ◽  
Human Stress ◽  
Stress States

scholarly journals Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory

2019 ◽  
Vol 30 (01) ◽  
pp. 1950027 ◽  
Author(s):  
Xiuhui Wang ◽  
Wei Qi Yan
Keyword(s):  
Short Term Memory ◽  
Gait Recognition ◽  
Large Population ◽  
Recognition Rate ◽  
Human Gait ◽  
Short Term ◽  
Term Memory ◽  
Cross Covariance ◽  
Novel Method ◽  
Long Short Term Memory

Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person’s gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

Effect of microcracking on the deformation of ice

1992 ◽  
Vol 29 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Ian J. Jordaan ◽  
Barry M. Stone ◽  
Richard F. McKenna ◽  
Mark K. Fuglem
Keyword(s):  
Strain Rate ◽  
Field Experiments ◽  
Constant Strain Rate ◽  
Creep Damage ◽  
Future Research ◽  
Strain Response ◽  
Short Term ◽  
Large Fluctuations ◽  
Response To Stress ◽  
Stress States

Tests in the field and full-scale experience with arctic structures show that the crushing of ice is accompanied by large fluctuations in load. Field experiments show that, in addition to variations of load in time, significant spatial variations across the contact surface also occur. The deformation is observed to take place in a thin layer of damaged ice, which appears near the structure or indenter surface. It is important to model the deformation and strength of ice in this zone. Various aspects of modelling are discussed in the paper, in particular, measures of damage and the relation to the deformation of ice. The relevance of various components of deformation (elastic, viscous, delayed elastic) is outlined, and two mathematical formulations for the deformation are discussed. The behaviour was investigated by a series of tests at constant strain rate as well as tests in which the strain response to stress of damaged and undamaged ice was measured. The creep rate in damaged ice is shown to be significantly enhanced, even for short-term loading. Comparisons of theory and experiment are given for constant strain-rate tests. The models have been calibrated to the experimental data described in the paper. It is a matter for future research to generalize the models to all damage levels and stress states. Key words : creep, damage, deformation, ice, microcracking, visco-elasticity.

771: Short-term surgical and clinical outcomes with a novel method for open fetal surgery of myelomeningocele

2015 ◽  
Vol 212 (1) ◽  
pp. S374 ◽  
Author(s):  
Antonio Moron ◽  
Mauricio Barbosa ◽  
Herbene Milani ◽  
Wagner Hisaba ◽  
Natalia Carvalho ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Fetal Surgery ◽  
Short Term ◽  
Novel Method

scholarly journals Predictive Analysis of Cryptocurrency Price Using Deep Learning

2018 ◽  
Vol 7 (3.27) ◽  
pp. 258 ◽  
Author(s):  
Yecheng Yao ◽  
Jungho Yi ◽  
Shengjun Zhai ◽  
Yuwen Lin ◽  
Taekseung Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
International Relations ◽  
Short Term Memory ◽  
Training Data ◽  
Short Term ◽  
Effective Learning ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Novel Method ◽  
Long Short Term Memory

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

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