scholarly journals A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 429 ◽  
Author(s):  
Minjia Li ◽  
Lun Xie ◽  
Zhiliang Wang
Keyword(s):  
Real Time ◽  
Wavelet Packet ◽  
Field Studies ◽  
Physiological Signals ◽  
Support Vector ◽  
Transductive Learning ◽  
Time Stress ◽  
Model Based ◽  
Training Examples ◽  
Stress Recognition

Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework.

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

scholarly journals Real-Time Stress Detection by Means of Physiological Signals

10.5772/18246 ◽  
2011 ◽  
Author(s):  
Alberto De ◽  
Carmen Snchez-Avila ◽  
Javier Guerra-Casanova ◽  
Gonzalo Bailador-Del
Keyword(s):  
Real Time ◽  
Physiological Signals ◽  
Stress Detection ◽  
Time Stress

scholarly journals Soft Sensor Model Based on IBA-LSSVM for Photosynthetic Bacteria Fermentation Process

2021 ◽  
Vol 25 (2) ◽  
pp. 145-158
Author(s):  
Khalil Ur Rehman ◽  
◽  
Xiang Lin Zhu ◽  
Bo Wang ◽  
Muhammad Shahzad ◽  
...  
Keyword(s):  
Real Time ◽  
Photosynthetic Bacteria ◽  
Differential Evolution Algorithm ◽  
Bat Algorithm ◽  
Soft Sensor ◽  
Learning Ability ◽  
Support Vector ◽  
Model Based ◽  
Sensor Model ◽  
Improved Model

It is difficult to measure the key biological process variables of photosynthetic bacteria fermentation in real-time, and offline measurement has a large time lag and cannot meet the needs of real-time optimization control. In this paper, a soft sensor model based on least square support vector machine with an improved bat algorithm (IBA-LSSVM) was proposed. The velocity equation of the bat algorithm (BA) was improved and the random variation operation in differential evolution algorithm was introduced into BA algorithm. Thus, the diversity of the population can be increased, and the global and local searching ability of the BA algorithm can be enhanced. Furthermore, the IBA-LSSVM soft sensor model was established for the living cell concentration and compared with BA-LSSVM soft sensor model. Finally, the simulation results show that the improved model was the better learning ability and prediction performance than BA-LSSVM, the measurement error is 0.1358. The improved model could provide accurate guidance for the photosynthetic bacteria fermentation control optimization. This model has certain practical value.

scholarly journals A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6369
Author(s):  
Junhong Wang ◽  
Shaoming Sun ◽  
Yining Sun
Keyword(s):  
Muscle Fatigue ◽  
Vastus Lateralis ◽  
Wavelet Packet ◽  
Classification Model ◽  
Threshold Function ◽  
Support Vector ◽  
Semg Signal ◽  
Recognition Model ◽  
Model Based ◽  
Lstm Network

Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

An improved compact propulsion system model based on batch normalize deep neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Juan Fang ◽  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Chongwen Jin
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Real Time ◽  
Data Storage ◽  
High Accuracy ◽  
Propulsion System ◽  
System Model ◽  
Support Vector ◽  
Baseline Model ◽  
Model Based

Abstract Aero-engine on-board steady state model is an important part of many advanced engine control algorithms. In order to build a high accuracy and real-time steady-state onboard model in a large envelope, an ICPSM (improved compact propulsion system model) based on batch normalize neural network is proposed in this paper. Compared with piecewise linearization model and support vector machine model, conventional CPSM which is mainly composed of baseline model and nonlinear sub model has the advantages of high real-time performance and small data storage. However, as the similarity conversion error increases with the distance from the design point, the cumulative error of the conventional baseline model also increases, which makes the model unable to maintain high accuracy in the full envelope. Thus, a high accuracy baseline model in full envelope based on batch normalize neural network is proposed in this paper. The simulation result shows that compared with the conventional compact propulsion system model, the percentage error of parameters of the improved compact propulsion system model based on the batch neural network is reduced by two times, the single step operation time is reduced by 18%, and the data storage of the onboard model is reduced as well.

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