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检索条件"机构=Dept. of Electrical and Computer Engineering and Dept. of Biomedical Informatics"

共 2517 条记录，以下是11-20 订阅

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Classifying Driver Distraction with Textile Electrocardiograms 46

Classifying Driver Distraction with Textile Electrocardiogra...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Pavan, Kaveti Roha, Vishal Singh Igasaki, Tomohiko Karthick, P.A. Pawar, Digvijay S. Ganapathy, Nagarajan Iit Dept. of Biomedical Engineering Telangana Hyderabad India Monash University Dept. of Electrical & Computer Systems Melbourne Australia Kumamoto University Dept. of Biomedical Engineering Kumamoto Japan Nit Dept. of Instrumentation Engineering Tamil Nadu Tiruchirappalli India Iit Dept. of Civil Engineering Telangana Hyderabad India

ISBN: (纸本)9798350371499

Textile sensor-based vital sign assessment plays an important role in continuous monitoring due to its unobtrusive and non-invasiveness. Textile electrocardiography (ECG) sensors allow mental wellbeing assessments in drivers during driving. In this study, we assess the effectiveness of a single-lead ECG obtained from a non-medical-grade ECG shirt for detecting driver distraction due to induced stress. Using ECG shirts, a single-lead ECG (256Hz, 12 bits) is acquired from N=10 healthy volunteers having driving licenses in three distinct driving situations (Baseline, Texting, Calling) in a controlled environment. ECG data is manually checked, and segmented into short durations (10, 30, 60 seconds). These segments are applied to a customized convolution neural network (ccNN). The proposed approach is able to classify the driver's distraction with ccNN yielding a weighted F-Score of 0.65 and an average accuracy of 67.12% on the validation set. Leave-One-Subject-Out Cross-Validation results showed weighted F-Scores ranging from 0.53 to 0.75. Thus, a single-lead, wearable textile ECG provides informative insights into a driver's mental wellbeing. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Feature Detection in Non-Proliferative Diabetic Retinopathy via RGB Channel Separation

Enhanced Feature Detection in Non-Proliferative Diabetic Ret...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Fernando, I.A.S.I. Induwara, M.S. Isuranga, T.H.N. Wijewardhana, U.L. Dissanayake, M.M. Balagalla, U.B. University of Sri Jayewardenepura Dept. Electrical & Electronic Engineering Nugegoda Sri Lanka University of Colombo Dept. Anatomy & Biomedical Informatics Colombo Sri Lanka

ISBN: (纸本)9798350350821

Image pre-processing is crucial in biomedical stage detection systems since it enhances significant features, supporting subsequent image segmentation and classification phases. Particularly, in Non-Proliferative Diabetic Retinopathy (NPDR), stage-defining characteristics are extremely small and image pre-processing is vital to enhance the features for accurate stage diagnosis. This paper discusses an RGB channel separation analysis carried out to identify how each channel caters to emphasizing NPDR features using a set of color fundus images. Several methods were tested manipulating channel intensities, and the best performing method was chosen via the results generated from a U-Net segmentation model which was trained for each tested method. The results were compared using the U-Net dice scores, IOU values, and the identified and unidentified pixel percentages of the difference image which was obtained by overlapping the U-Net output image and the ground truth image. The best performing method presented a dice score of 0.80 and an IOU value of 0.66 on average. © 2024 IEEE.

关键词： RGB color model

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Feature engineering for Optimizing AI-Driven Classification Models in Non-Proliferative Diabetic Retinopathy Stage Detection 6

Feature Engineering for Optimizing AI-Driven Classification ...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Induwara, M.S. Fernando, I.A.S.I. Isuranga, T.H.N. Wijewardhana, U.L. Dissanayaka, M.M. Balagalla, U.B. University of Sri Jayewardenepura Dept. Electrical and Electronic Engineering Nugegoda Sri Lanka University of Colombo Dept. Anatomy and Biomedical Informatics Colombo Sri Lanka

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389692

Early detection of the stages of Non-Proliferative Diabetic Retinopathy (NPDR) is crucial for timely treatment, which can prevent the progression to severe stages and vision loss from the disease. AI shows significant potential in automating the detection of NPDR stages with enhanced efficiency and accuracy using fundus images. This paper presents an AI-driven approach for the automatic stage detection of NPDR using a hierarchical method of Random Forest (RF) machine learning models combined with feature engineering techniques. Three RF models, each serving as a binary classifier, were employed with their feature scores to study the influence of extracted features from image segmentation on the classification stages of NPDR. Utilizing 17 features that are commonly used in NPDR classification, the optimal feature combination, indicating the highest accuracy for each classification stage, was identified through the tested methods. The hierarchical RF model with the selected high-performing feature sets achieved an overall accuracy of 95.1%, while the all-class RF classifier accuracy was only 86.9%. The results highlight the impact of feature selection on automatic NPDR classification while providing a robust model with improved classification accuracy using the selected optimal feature sets. © 2024 IEEE.

关键词： Image segmentation

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On the Role of ANN on the Estimation of Payload Inertia in Parallel Kinematic Machine 3

On the Role of ANN on the Estimation of Payload Inertia in P...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Fuentes, José Alfonso Farías Giuletti, Nicola Carnevale, Marco Giberti, Hermes Università di Pavia Dept. Electrical Computer and Biomedical Engineering Pavia Italy Università di Pavia Dept. Industrial and Information Engineering Pavia Italy

ISBN: (纸本)9798350378009

In the robotic field, accurately estimating the inertial parameters of a robot payload is essential for improving path-following precision in model-based robot control. Nowadays, there are two main approaches to address this problem. One approach involves using direct measurement of the interaction between the robot End-Effector and the payload in terms of motions and exchanged forces-torques. The other approach involves a standard dynamic robot identification problem, considering the payload as an extension of the last robot link. Several of these techniques have been tested and consolidated for serial robots. However, results obtained for the estimation using Parallel Kinematic Machines have been less accurate than those using serial robots, especially when no direct measurement is considered, due to the complexity of their kinematic chain and the greater impact of uncertainties in their geometrical and dynamic parameters. The purpose of this work is to evaluate the performance of an approach for estimating the inertial properties of the payload using a trained data-driven model, namely an Artificial Neural Network (ANN). This model takes as input the same information available to the machine when direct measurements are made (i.e., positions and forces exchanged between the End-Effector and the payload) and returns as output the inertia tensor and center of mass of the payload. The developed method is validated and compared with standard model-based approaches (e.g., Least Square Method based on the Newton-Euler equations). This work is the first step towards developing a novel approach for estimating the inertial properties of the robot payload using data-driven models with the inputs of the indirect method (i.e., actuator's motion and torque), in order to use as few sensors as possible. © 2024 IEEE.

关键词： Euler equations

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TexSense: Comparative ECG Analysis of E-Textile and Sticky Electrodes During Motion Artifacts

TexSense: Comparative ECG Analysis of E-Textile and Sticky E...

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2024 International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles, E-Textiles 2024

作者： Al Rumon, Md Abdullah Cerullo, Alexander Berube, Leah Ravichandran, Vignesh Shahriari, Yalda Solanki, Dhaval Mankodiya, Kunal University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9783000804298

The rapid advancement of wearable technology is transforming health monitoring and driving demand for more comfortable, long-term ECG solutions. Traditional sticky or gel electrodes (SE), while highly accurate in stationary medical environments, often cause discomfort and skin irritation with prolonged adhesive use. Textile-based dry electrodes (TE) eliminate the need for conductive gel, offering a promising alternative for continuous ECG monitoring in dynamic conditions. However, the performance of textile electrodes in real-world settings remains largely unexplored, highlighting a critical research gap in understanding how dry electrodes compare to sticky electrodes when motion artifacts are present. This study investigated a comparative evaluation of gel-based sticky and textile dry electrodes during exercise-induced motion artifact tasks. We performed the study with 10 adult healthy participants and designed 16 distinctive motion artifact tasks that included reclining, sitting, walking, and running. The qualitative, and quantitative analysis illustrated the comparison of ECG signal, Heart Rate (HR) detection accuracy and error%, signal quality, SNR, Short-Time Fourier Transform (STFT), Kurtosis, Entropy, Continuous Wavelet Transform (CWT), and Bland-Altman analysis. The analysis showed textile electrodes achieved an HR detection accuracy of approximately 99% and an average Signal-to-Noise Ratio (SNR) of 15.8 dB. This performance approaches the 17.3 dB average of sticky electrodes, demonstrating strong resilience to noise in low-to-moderate motion tasks. The STFT analysis indicated reliable capture of essential QRS frequency components (5-15 Hz) by textile electrodes, though high-motion tasks produced slight deviations that may limit detailed QRS morphology analysis. The CWT analysis revealed the TE showed an average magnitude difference of 0.15 in low-motion tasks to 0.5 in high-motion tasks compared to SE, indicating increased noise sensitivity above 30 Hz. The

关键词： Electrotherapeutics

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Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform 46

Classification of Emerging Neural Activity from Planning to ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Cetera, Anna Rabiee, Ali Ghafoori, Sima Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

There have been different reports of developing Brain-computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively. © 2024 IEEE.

关键词： Filter banks

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Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types 46

Bispectrum Analysis of Noninvasive EEG Signals Discriminates...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ghafoori, Sima Rabiee, Ali Cetera, Anna Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

The bispectrum stands out as a revolutionary tool in frequency domain analysis, leaping the usual power spectrum by capturing crucial phase information between frequency components. In our innovative study, we have utilized the bispectrum to analyze and decode complex grasping movements, gathering EEG data from five human subjects. We put this data through its paces with three classifiers, focusing on both magnitude and phase-related features. The results highlight the bispectrum's incredible ability to delve into neural activity and differentiate between various grasping motions with the Support Vector Machine (SVM) classifier emerging as a standout performer. In binary classification, it achieved a remarkable 97% accuracy in identifying power grasp, and in the more complex multiclass tasks, it maintained an impressive 94.93% accuracy. This finding not only underscores the bispectrum's analytical strength but also showcases the SVM's exceptional capability in classification, opening new doors in our understanding of movement and neural dynamics. © 2024 IEEE.

关键词： Frequency domain analysis

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Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning 46

Precision Enhancement in Sustained Visual Attention Training...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Norouzi, Maryam Amirani, Mohammad Zaeri Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future. © 2024 IEEE.

关键词： Wavelet transforms

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Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types 46

Wavelet Analysis of Noninvasive EEG Signals Discriminates Co...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Rabiee, Ali Ghafoori, Sima Cetera, Anna Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications. © 2024 IEEE.

关键词： Wavelet analysis

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network 24

Emotion Recognition from Video Frame Sequence using Face Mes...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Derry Pramono Yuniarno, Eko Mulyanto Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe's face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of 'Anger,' 'Fearful,' 'Happy,' and 'Sad.' For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of ∼2.3 seconds (achieved using the SqueezeNet model). © 2023 IEEE.

关键词： Emotion Recognition

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