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Stress Detection by Deep Learning Technique

Stress Detection by Deep Learning Technique

引用

IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Madduri Tarun Vimala Kumari Jonnalagadda Ayinapuru Jaswitha Sai Kodali Nivas Prattipati Vamsi Mohan Electrical and Electronics Engineering V. R. Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Stress is a natural response of the body to challenging situations or demands, whether they are physical or psychological in nature. It is a condition of emotional or mental stress that can be brought on by a number of things, including pressure from the workplace, worries about money, relationships, health, or significant life events. It's important to recognize symptoms of stress and take steps to manage stress before it starts to have a negative impact on our health and wellbeing. Several existing solutions exist for detecting stress levels, such as Natural Language processing (NLP), speech analysis, image, and video analysis, etc., to detect the stress level of a person based on machine learning. This method is based on Convolution Neural Networks (CNNs). CNN is a kind of deep learning technique that works well for analyzing pictures and videos. CNNs can be used for stress detection by analyzing image and video data to identify physical markers of stress, such as changes in facial expressions, posture, and eye movements, as involving CNNs the accuracy of an algorithm will be improved to a greater extent. This research investigates the use of deep learning methods to identify stress in individuals.

关键词： Deep learning Transient response Speech analysis Refining signal processing algorithms Human factors Solids

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SOS-MUSIC, a Subspace Approach for EEG Source Imaging Promoting Sparsity of Active Sources

SOS-MUSIC, a Subspace Approach for EEG Source Imaging Promot...

引用

European signal processing Conference (EUSIPCO)

作者： Carla Joud Laurent Albera Isabelle Merlet Julie Coloigner Univ Rennes INSERM LTSI-UMR 1099 Univ Rennes INRIA CNRS IRISA INSERM Empenn U1228 ERL Univ Rennes INSERM LTSI-UMR 1099 Rennes France Univ Rennes INRIA CNRS IRISA INSERM Empenn U1228 ERL Rennes France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Localizing multiple synchronous brain current sources from ElectroEncephaloGraphy (EEG) recordings is a challenging problem in presurgical evaluation of certain diseases such as drug resistant epilespy. In this paper, we propose a novel MUSIC-like (MUltiple signal Classification) EEG source imaging method, named SOS-MUSIC. The latter minimizes the MUSIC metric as well as promoting Sparsity Of active Sources (SOS) to enhance performance. Indeed, by this way SOS-MUSIC helps to deal with synchronous (i.e. totally correlated) brain current sources, unlike classical approaches. This is illustrated through realistic computer simulations in the context of epilepsy by analyzing two challenging situations (low signal-to-noise ratios or correlated brain sources).

关键词： Measurement Resistance Computer simulation signal processing algorithms Imaging Pattern classification Electroencephalography Multiple signal classification Recording signal to noise ratio

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Evaluation of a Multi-Sensor Approach for Drowsiness Monitoring

Evaluation of a Multi-Sensor Approach for Drowsiness Monitor...

引用

European signal processing Conference (EUSIPCO)

作者： Vincent Beauchamps Bastien Berthelot Vincent Ibanez Fabien Sauvet Institut de recherche biomedicale des armées (IRBA) Brétigny sur Orge France RRP 7330 VIFASOM Université de Paris Hotel Dieu Paris France THALES AVS France Campus Merignac France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

For the past few years, a significant interest has been paid to crew monitoring systems to enhance flight safety. One of the key aspects is assessing the level of drowsiness of crew members. To address this, some airlines adopt a prescriptive approach through the use of biomathematical models, while others consider testing solutions based on analyzing the crew's neurophysiological signals. In this paper, our contribution is threefold. First, we introduce a novel drowsiness monitoring system based on cardiac and ocular analyses. It is autonomous, objective and provides a non-invasive and continuous monitoring. Second, we present a protocol alongside experiments designed to test this system. Third, we conduct an in-depth analysis of the performances of the aforementioned algorithm compared to mono-sensor approaches on the collected test dataset. The results demonstrate that, on the test dataset, the multi-sensor model offers promising performance and correlation with reference drowsiness metrics, with 75% accuracy and 0.88 Area Under Curve in a three classes classification and a correlation coefficient of 0.57, outperforming mono-sensor models on the same dataset.

关键词： Measurement Protocols Machine learning algorithms signal processing algorithms Europe Estimation signal processing Safety Monitoring Testing

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Line-Structured Light Based 3D Reconstruction Algorithm for Ice Surface

Line-Structured Light Based 3D Reconstruction Algorithm for ...

引用

International Conference on Vision, Image and signal processing (ICVISP)

作者： Chengzhi Jiang Zhen Ren Yuxiang Li Zhisheng Gao School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Line structured light has a wide range of applications in 3D measurement, such as metal parts, weld seams and other precision shaft parts, and the surface of ice cubes has similar characteristics and can also be measured in 3D using line structured light. However, due to the distinctive optical characteristics of the ice surface, the brightness of the captured laser stripes at certain positions may be either insufficient or excessively intense, posing difficulties in accurately identifying the centerline of the laser stripes; and the high transparency of the ice surface, part of the light will be projected into the interior of the ice resulting in a weakened reflection, and the light projected into the interior of the light will also diffuse and affect the imaging. Therefore, how to accurately measure the three-dimensional structure of the surface has become a topic to be studied. To accurately identify the centerline of the laser stripes within the interference image, this paper introduces a laser stripe center extraction algorithm that combines deep learning with the Steger algorithm. By leveraging the robust feature learning capabilities of deep neural networks, the algorithm can effectively capture the overall characteristics of the laser stripes on the ice surface, enabling precise detection and segmentation of the stripes. Subsequently, the Steger algorithm is applied to determine the centerline of the laser stripes with high accuracy. Subsequently, Steger's algorithm is employed to precisely determine the centerline of the laser stripes. This approach effectively mitigates the problem of not being able to accurately extract the center of the laser stripes caused by the high transparency of ice and complex surface reflection characteristics. Subsequent experiments have proved that this method can greatly reduce the extraction time of laser stripe center and significantly improve the accuracy of laser stripe center extraction and 3D reconstruction.

关键词： Surface reconstruction Three-dimensional displays Accuracy Laser noise Ice surface Lasers signal processing algorithms Measurement by laser beam Surface emitting lasers Convolutional neural networks

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Diagnosis of Plant Leaf Disease using Vision Transformer

Diagnosis of Plant Leaf Disease using Vision Transformer

引用

International Conference on Communications and signal processing

作者： Ashik Sundaraj Deva Priya Isravel Julia Punitha Malar Dhas Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield.

关键词： Training Plant diseases Plants (biology) Crops signal processing algorithms signal processing Transformers

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Bearing and gear monitoring under variable speeds and loads using regime normalization

Bearing and gear monitoring under variable speeds and loads ...

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Annual Conference of Industrial Electronics Society

作者： Mahfoud Bouzouidja Moncef Soualhi Abdenour Soualhi LASPI Université Jean Monnet Saint-Etienne IUT de Roanne Roanne France SUPMICROTECH CNRS Institut FEMTO-ST Université de Franche-Comté Besaçon France

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In practice, rotating machines operate under real-time variable speed and load conditions, particularly in railways, wind turbines, robotics, etc. In the presence of faults, the degradation of critical components is significantly accelerated, making the developments of robust monitoring algorithms crucial to be able to identify these faults with regard to regime variation. For this purpose, a signal processing methodology is proposed. It is based on regime normalization that allows the grouping of different regimes belonging to the same health state. It consists of a thresholding method that detect condition variation and uses distance metrics for normalization. This, allows reducing dispersion between the class observations while separating the classes representing different health states. To verify the performance of the methodology, current, voltage and vibration data from a gearbox system under variable speeds and loads are used.

关键词： Vibrations Gears Rotating machines signal processing algorithms Voltage Feature extraction Wind turbines Monitoring Dispersion Robots

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Decoding Energy Optimization for Video Coding Using Model-Driven Gradient Descent

Decoding Energy Optimization for Video Coding Using Model-Dr...

引用

IEEE Workshop on Multimedia signal processing

作者： Christian Herglotz Matthias Kränzler Bide Xu André Kaup Computer Engineering Brandenburgische Technische Universität Cottbus-Senftenberg Germany Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg Germany

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Nowadays, a large part of the global energy consumption caused by video communications can be attributed to end-user devices such as smartphones, tablet PCs, and TV sets. In this paper, we present a method to increase the performance of an existing algorithm dedicated to reduce the end-user side energy consumption during video streaming. The algorithm, which is called decoding-energy-rate-distortion optimization (DERDO), exploits a decoding energy model during encoding and chooses coding modes in such a way that the software decoding energy is minimized. In this paper, we develop a dedicated gradient descent approach for DERDO that refines specific energy coefficients used for decoding energy modeling. We find that this approach boosts the performance of DERDO by increasing the energy savings by at least 5% with respect to standard DERDO. As a consequence, we observe decoding energy savings of more than 40% and more than 7% for practical encoder and decoder implementations of HEVC and H.264/AVC, respectively, when compared to standard encoding using classic rate-distortion optimization.

关键词： Energy consumption Energy conservation Software algorithms signal processing algorithms Streaming media Encoding Software Decoding Optimization Standards

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A Scheme of Low-Complexity and High-Precision Heartbeat Rate Detection Based on FMCW Radar

A Scheme of Low-Complexity and High-Precision Heartbeat Rate...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Hui Yu Min Zhou Basic training department of Yantai Vocational College Physics and electronic information of Yantai University

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

With the development of radar technology and the trend towards non-contact detection, the frequency modulated continuous wave (FMCW) radar has been used for vital signs detection. In this paper, we propose an innovative approach aimed at enhancing the precision and robustness of heartbeat rate estimation. Firstly, we propose a range bin selection methodology that specifically targets the range bin that encapsulates the most pertinent information pertaining to human vital signs. Secondly, aiming at the problem that the target human body spans multiple range bins, a multi-range bin signal fusion method is proposed. Finally, to improve the accuracy of heartbeat signal frequency measurement derived from the Variable Mode Extraction (VME) algorithm, we propose a novel method called CZT-Jacobsen. Refers to the heartbeat rate obtained by the contact detection instrument, the mean absolute error (MAE) of the proposed scheme in our experiment is 1.433 bpm on average, which achieves a better detection performance than the traditional scheme.

关键词： Accuracy Radar detection Estimation signal processing algorithms Radar signal processing Market research Robustness Heart rate variability signal to noise ratio

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Research on Nonnegative Matrix Decomposition Spectrum Perception Method Based on Convolutional Neural Networks

Research on Nonnegative Matrix Decomposition Spectrum Percep...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Dongmei Duan Fan Zhou Xingye Qian Shaoqiang Wang School of Information Science and Engineering Shenyang Ligong University Shenyang China Qingdao Ligong University Qingdao China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to improve the spectrum sensing performance of cognitive radio systems under low signal-to-noise ratio conditions, a non negative matrix decomposition spectrum sensing method based on convolutional neural networks is proposed. The core idea of this method is to use convolutional neural network structure to effectively extract spectral features, and combine non negative matrix decomposition technology to decompose and reconstruct the observed signals. Through this approach, noise and signal can be effectively distinguished, thereby improving the system's perception of the surrounding spectral environment. The simulation results show that this method has better perceptual performance than existing methods at a signal-to-noise ratio of -25dB~-5dB.

关键词： Time-frequency analysis Fourier transforms Simulation Gaussian noise signal processing algorithms Feature extraction Sensors Matrix decomposition Convolutional neural networks signal to noise ratio

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TBDM-NET: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition

TBDM-NET: Bidirectional Dense Networks with Gender Informati...

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IEEE Workshop on Machine Learning for signal processing

作者： Vlad Striletchi Cosmin Striletchi Adriana Stan Communications Department Technical University of Cluj-Napoca Romania

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

This paper presents a novel deep neural network-based architecture tailored for Speech Emotion Recognition (SER). The architecture capitalises on dense interconnections among multiple layers of bidirectional dilated convolutions. A linear kernel dynamically fuses the outputs of these layers to yield the final emotion class prediction. This innovative architecture is denoted as TBDM-Net: Temporally-Aware Bi-directional Dense Multi-Scale Network. We conduct a comprehensive performance evaluation of TBDM-Net, including an ablation study, across six widely-acknowledged SER datasets for unimodal speech emotion recognition. Additionally, we explore the influence of gender-informed emotion prediction by appending either golden or predicted gender labels to the architecture's inputs or predictions. The implementation of TBDM-Net is accessible at: https://***/adrianastan/tbdm-net.

关键词： Performance evaluation Emotion recognition Accuracy signal processing algorithms Speech recognition Computer architecture signal processing Prediction algorithms Real-time systems Speech processing

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