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2023 IEEE International Conference on Big Data, BigData 2023

作者： Tran, Tan Chang, Lin-Ching Lum, Peter S. The Catholic University of America School of Engineering Department of Electrical Engineering and Computer Science WashingtonDC United States The Catholic University of America School of Engineering Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350324457

The rehabilitation of the upper extremity (UE) plays a pivotal role in the recovery process for stroke survivors. Despite the routine practice of UE rehabilitation, assessing its effectiveness poses a significant challenge. Evaluation methods vary, ranging from clinical assessments of UE motor skills and patient feedback to the utilization of wearable technology equipped with accelerometer or gyro sensors. In this study, we employed deep learning approaches such as Multi-Layer Perceptron, Long Short-term Memory, and Gated Recurrent Units, in conjunction with data from accelerometry sensors embedded in wrist-wearable Inertial Measurement Units. Our findings demonstrate the successful differentiation between functional and non-functional UE movements in individuals recovering from strokes with 90% and 74% of classification accuracy for intra-subject and inter-subject models, respectively. The methodologies employed in this study exhibit consistency and reliability, providing precise outcomes for individual and acceptable outcomes for cross-participant evaluations. Notably, our advanced models directly utilized raw accelerometry data instead of manual extracted features that was often used in traditional machine learning algorithms. These innovative techniques offer cost-effective and adaptable tools for monitoring UE functionality in real-world settings. The insights derived from this study hold the potential to be transformative in tailoring rehabilitation strategies for adults affected by strokes. © 2023 IEEE.

关键词： deep learning rehabilitation stroke upper extremity

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Composite Power System Reliability Assessment Considering Uncertainty of Electric Vehicle Charging and PV Power Generation

Composite Power System Reliability Assessment Considering Un...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Thapa, Jitendra Olowolaju, Joshua Livani, Hanif Benidris, Mohammed Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States University of Nevada Department of Electrical & Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9798350320169

Modern power systems are facing several challenges with the increasing penetration of inverter-based resources (IBRs) and electric vehicles (EVs). The problem arises due to inter-mittency and uncertainties associated with IBR-based resources and electric vehicles. It is indispensable from the perspective of power system planning and operation to consider these factors in reliability assessment. The reliability assessment of modern power systems that are heavily penetrated with renewable generators should consider their uncertainties to quantify the reliability index and include EVs charging load in their framework as well. Therefore, this paper proposes an approach for composite power system reliability assessment combined with the probabilistic prediction interval of PV power along with the integration of electric vehicles. An electric vehicle load model developed considering the different locations of charging stations, types of EV, and drivers' behavior is integrated. Finally, a sequential Monte Carlo simulation is used to determine the range of the reliability index after an interval forecast of the PV and EV load model is developed. The impact of different levels of EV penetration on the reliability of power system integrated with PV is also investigated. The significance of this work is to provide an accurate reliability assessment framework that takes PV uncertainties and electric vehicles into consideration. © 2023 IEEE.

关键词： Forecasting

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Voltage and Current Feedforward Terms Impact in the Stability of Grid-Forming Inverter Systems

Voltage and Current Feedforward Terms Impact in the Stabilit...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Benfatto, Oriana Tresca, Giulia Formentini, Andrea Anglani, Norma Cossu, Simone Zanchetta, Pericle University of Pavia Electrical Computer and Biomedical Engineering Dept. Pavia Italy University of Genoa El. Elec. and Telec. Engineering and Naval Architecture Dept. Genoa Italy

ISBN: (纸本)9798350376067

This paper explores the issues related to the stability of a grid-connected three-phase grid-forming inverter embedded in a microgrid. The core analysis emphasizes the optimization of system controls and the influence of feed-forward terms on system stability. Through a comprehensive simulation and pole analysis, this paper demonstrates that feed-forward terms significantly affect the stability of the system. © 2024 IEEE.

关键词： Poles

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Cochlear Decomposition: A Novel Bio-Inspired Multiscale Analysis Framework 48

Cochlear Decomposition: A Novel Bio-Inspired Multiscale Anal...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Alfalahi, Hessa Khandoker, Ahsan Alhussein, Ghada Hadjileontiadis, Leontios Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9781728163277

Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal information in time-domain signals. So far, various SMD approaches, such as the Multiresolution Wavelet Transform (MWT), the Empirical Mode Decomposition (EMD), and the Variational Mode Decomosition (VMD) have been proposed. However, issues, such as mode mixing for signals with closelyspaced modes, have been identified. To confront such problems, we propose here a novel spatial auditory decomposition framework for non-stationary signals, namely the Cochlear Decomposition (CD). CD is inspired by the biological rules of the spiral human cochlea and it is built upon the concept of 'place-pitch' or the tonotopic organization of the spiral cochlea. The new insight here is to formulate a set of basis functions, namely cochlear wavelets, whose dilation factors are determined by their angular position on the cochlear spiral (i.e., coupled rotation and dilation). Under proper parameterization, iterative application of spatial filters eventually results in signal's mono-components, each arising from specific angular positions along the spiral cochlea, with high time and frequency localization. The performance of the proposed CD is validated via synthetic acoustic signals and real non-stationary speech signals analysis. The analysis results show that CD outperforms the performance of MWT, EMD, and VMD, exhibiting, at the same time, high noise robustness. We also show that the CD disentangles lowfrequency temporal modulations of sounds, supporting perceptual phenomena in both speech and music. Clearly, CD paves the way for higher-level speech perception deep learning models and also for efficient cochlear implants design. Future work to incorporate the nonlinearity of the cochlea into a binaural hearing framework is underway. © 2023 IEEE.

关键词： Audition

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The good, the bad and the ugly sides of data augmentation: an implicit spectral regularization perspective

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The Journal of Machine Learning Research 2024年第1期25卷 4470-4554页

作者： Chi-Heng Lin Chiraag Kaushik Eva L. Dyer Vidya Muthukumar School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA Coulter Department of Biomedical Engineering School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA School of Electrical & Computer Engineering School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta GA

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.

关键词： data augmentation generalization analysis overparameterized models spectral analysis regression classification

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ElboSense: A Novel Capacitive Strain Sensor for Textile-Based Elbow Movement Monitoring 19

ElboSense: A Novel Capacitive Strain Sensor for Textile-Base...

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19th IEEE International Conference on Body Sensor Networks, BSN 2023

作者： Al Rumon, Md Abdullah Ravichandran, Vignesh Veeturi, Suparna Owens, Jim Kumar, Deepesh Solanki, Dhaval Mankodiya, Kunal University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States Nautilus Defense PawtucketRI United States IIT-BHU School of Biomedical Engineering India

ISBN: (纸本)9798350338416

The field of wearable sensors has witnessed remarkable progress in recent years, enabling real-time monitoring of musculoskeletal biomechanics. Musculoskeletal injuries require long-term management through medications and physical therapy to ensure a smooth recovery. Assessment of a patient's range of motion (ROM) is an essential component of physical therapy. However, existing methods for measuring ROM are either analog or require expensive equipment. In this work, we present ElboSense, a smart textile-based sensing solution to monitor elbow motion precisely. ElboSense is embedded with a novel yarn-like capacitive strain sensor made with braided composite structures of multiple filament strands in a helical pattern. We seamlessly integrated the sensor into an elbow brace and developed an embedded system for data acquisition. The work conducted a study with eight healthy adult participants who performed a regulated elbow movement exercise (flexion and extension) 10 times. We validated the sensor measurements against a reliable optical motion-capture system. The average cross-correlation was found to be 0.91. Pearson's correlation coefficient analysis showed 0.95 for peak-to-peak and 0.98 for valley-to-valley. All the preliminary results indicate a good agreement and reliability as a potential sensing solution to monitor the range of motion during elbow exercises. © 2023 IEEE.

关键词： Wearable sensors

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Grid-Aware Waveform Analytics for Event Classification in Distribution Grids

Grid-Aware Waveform Analytics for Event Classification in Di...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Lakouraj, Mohammad Mansour Hosseinpour, Hadis Livani, Hanif Benidris, Mohammed University of Nevada Reno Department of Electrical & Biomedical Engineering RenoNV United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

This study proposes a new method for event sit-uational awareness in distribution grids using Synchro Wave-form Measurement Units (SWMUs). An efficient feature-extracting technique named the Short-Time Matrix Pencil method (STMPM) is used to capture the oscillation modes and distortion of voltage and current measurements under common events such as transient switching events and challenging high impedance faults. The extracted features from the waveform data are then used as the input to Graph Neural Network (GNN) as the event classifier. GNN captures the spatial relationship between SWMUs and physical features of the network to enhance event classification accuracy. The proposed grid-aware waveform analytics is tested for classifying different events, and the superior performance of the proposed method with respect to other approaches is verified using the classification merits, such as accuracy, Fl-score, precision, and recall. © 2023 IEEE.

关键词： Graph neural networks

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On a Sparse Shortcut Topology of Artificial Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 595-608页

作者： Fan, Feng-Lei Wang, Dayang Guo, Hengtao Zhu, Qikui Yan, Pingkun Wang, Ge Yu, Hengyong Rensselaer Polytechnic Institute Department of Biomedical Engineering TroyNY12180 United States University of Massachusetts Department of Electrical and Computer Engineering LowellMA01854 United States

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this article, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps understand the role of shortcuts and suggests further opportunities to innovate neural architectures. © 2020 IEEE.

关键词： Topology

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Assisting Upper Limb Prosthesis with a computer Vision System for Material Detection 46

Assisting Upper Limb Prosthesis with a Computer Vision Syste...

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

作者： Roper-Bryant, Dezmun Berman, Joseph Huang, He North Carolina State University Department of Electrical and Computer Engineering United States North Carolina State University University of North Carolina at Chapel Hill Joint Department of Biomedical Engineering United States

ISBN: (纸本)9798350371499

The concept of utilizing computer vision to aid in the control of prosthetic limbs has been explored in recent studies. However, many computer vision systems are challenged by noisy and cluttered backgrounds in realistic scenarios. In this study, we investigated the feasibility of using a state-of-the-art object detection model (YOLOv8) to identify objects grasped by a myoelectric prosthetic hand. A custom model was trained to recognize various types of cups and was integrated with an EMG-based prosthesis controller. The classification accuracy of the model during an object grasping task with a human subject was approximately 93%. Predictions of grasped objects will be used in future work to adjust the parameters of a grip force controller for a prosthetic hand based on the detected object's properties. © 2024 IEEE.

关键词： Artificial limbs

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Decision trees

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