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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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Polyvinyl Alcohol Cryogels (PVA-C): Preparation and Multimodal Imaging Applications

Polyvinyl Alcohol Cryogels (PVA-C): Preparation and Multimod...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Qi, Olivia R.Y. Peters, Terry M. Moore, John Chen, Elvis C.S. Western University Canada Robarts Research Institute London Canada School of Biomedical Engineering Western University Canada Department of Medical Biophysics Western University Canada Department of Electrical and Computer Engineering Western University Canada

ISBN: (纸本)9781510685949

Polyvinyl alcohol cryogel (PVA-C) is a widely used tissue-mimicking material in medical imaging phantoms, essential for evaluating imaging techniques, training clinicians, and simulating anatomical tissue structures. Its tunable mechanical and imaging properties make it valuable for multimodal imaging. However, PVA-C phantom reproducibility remains challenging due to fabrication variations, including inconsistencies in freeze-thaw cycle (FTC) conditions, PVA concentration, and imaging additives. This study systematically examines how PVA concentration, FTC profiles, and contrast-enhancing additives affect the mechanical and imaging properties of PVA-C phantoms in ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT). Using in-house experiments and a comprehensive literature review, we analyze FTC conditions' role in modulating stiffness, attenuation, and relaxation times. We also assess contrast agents such as barium sulfate, gadolinium, and silica-based scatterers on imaging visibility and result. Our findings identify optimal fabrication conditions that enhance PVA-C phantom reproducibility and performance across imaging modalities. Our findings offer a comprehensive framework for optimizing PVA-C phantoms, ensuring reliable multimodal imaging applications. © 2025 SPIE

关键词： computerized tomography

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A mathematical approach to mitigate the impact of compression waves in shear wave elastography 12

A mathematical approach to mitigate the impact of compressio...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Asemani, Hamidreza Rolland, Jannick P. Parker, Kevin J. The Institute of Optics University of Rochester RochesterNY United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Department of Biomedical Engineering University of Rochester RochesterNY United States

ISBN: (纸本)9781510683907

Shear wave elastography (SWE) has emerged as a valuable imaging technique across several modalities such as ultrasound, magnetic resonance imaging, and optical coherence tomography. Its capacity to assess tissue stiffness and identify lesions positions it as a promising clinical tool for diagnosing a wide range of diseases. The primary aim of SWE is to measure shear wave speed (SWS);however, unwanted compression waves and bulk tissue motion can complicate this process. Conventional methods, in many cases, face challenges in separating shear and compression waves, resulting in inaccurate SWS measurements. This study introduces a novel estimator, called integrated difference autocorrelation (IDA), designed to specifically estimate reverberant shear wave speed in the presence of compression waves and noise. Unlike conventional methods, the IDA estimator computes the velocity differences between adjacent particles, effectively mitigating the impact of long-wavelength compression waves and other wide-area movements like those induced by respiration. The effectiveness of the IDA estimator was assessed through several approaches: elastography simulation of a y-shaped cylinder in a soft background, magnetic resonance elastography (MRE) on a brain phantom with two lesions, ultrasound elastography on a breast phantom with a lesion, and ultrasound elastography in the human liver-kidney region. Our findings indicate that the IDA estimator provides accurate SWS estimates (within 19% of reference) even in the presence of strong compression waves that can cause earlier estimators to completely fail. Moreover, the IDA estimator demonstrates consistent performance across different modalities and excitation scenarios, underscoring its robustness and potential clinical applicability. © 2025 SPIE.

关键词： Shear waves

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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations 63

A Model-Based Approach for Improving Reinforcement Learning ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ozcan, Erhan Can Giammarino, Vittorio Queeney, James Paschalidis, Ioannis Ch. Boston University Division of Systems Engineering BostonMA02215 United States CambridgeMA02139 United States Boston University Division of Systems Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations. © 2024 IEEE.

关键词： Deep reinforcement learning

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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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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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Building Robust Fraud Detection Model for Insurance Claims Using SMOTE 3rd

Building Robust Fraud Detection Model for Insurance Claims U...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Malathi, M. Armash, S. M. Mohammed Kavitha, S. Chinthamani, B. Sinthia, P. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India UG Scholar Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Saveetha Engineering College Chennai India Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Biomedical Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819738090

The insurance fraud claim detection is a critical task in the insurance industry. Various methodologies were proposed in terms of detecting fraud claims but the main thing is handling the proportionality between the fraud and non-fraud cases in the dataset before it was given to the model, in that case class imbalance may occur. Class imbalance where the number of fraudulent claims is significantly lower than legitimate claims which pose a challenge for accurate fraud detection. In this paper, we employed [Elreedy and Atiya in Inf Sci 505:32–64, 2019] Synthetic Minority Over-sampling Technique (SMOTE) algorithm to address the imbalance problem and enhance the performance of the fraud claim detection model. SMOTE is a popular technique for oversampling the minority class by creating synthetic samples that are similar to the existing minority class instances. By generating synthetic examples, SMOTE helps in balancing the class distribution and allows the model to learn from a more representative dataset. This technique is particularly effective when the available data is limited and insufficient to capture the complexities of the limited class. This process results in a larger and more balanced dataset, enabling the model to learn from a diverse range of fraudulent claim patterns. By applying SMOTE to our dataset, we are able to overcome the class imbalance issue and improve the performance of the fraud claim detection model. The resampled dataset provides a more accurate representation of the underlying distribution, leading to enhanced detection of fraudulent claims. We evaluate the performance of the model by measuring various metrics such as accuracy, precision, recall and F1-score. Our findings demonstrate the effectiveness of SMOTE in addressing class imbalance with improved random forest and LightGBM model for fraud claim detection process. The utilization of SMOTE contributes to better identification of fraudulent insurance claims, reducing potential losses an

关键词： Crime

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Segmentation Approach for Enhanced DLP Bioprinting 12

Segmentation Approach for Enhanced DLP Bioprinting

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Laser 3D Manufacturing XII 2025

作者： Babayigit, Ceren Tavares-Negrete, Jorge Alfonso Esfandyarpour, Rahim Boyraz, Ozdal Department of Electrical Engineering and Computer Science The University of California IrvineCA92697 United States Department of Biomedical Engineering The University of California IrvineCA92697 United States Department of Mechanical and Aerospace Engineering The University of California IrvineCA92697 United States

ISBN: (纸本)9781510684560

Achieving high-resolution outputs in Digital Light Processing (DLP) bioprinting requires precise control over photocrosslinking, particularly for dense, intricate models with fine gaps. Over-crosslinking is affected by factors such as light scattering, limited depth of focus, and molecular diffusion. Traditionally, these issues are managed by altering the material and light source. This study presents a novel segmentation approach designed to address these challenges by leveraging the inherent diffusion characteristics of the DLP system rather than altering its core components. The generation and diffusion of free radicals increase with light intensity and the density of the illuminated area, which alters the risk of over-crosslinking in these regions. The proposed technique involves segmenting the photomask into discrete regions and employing an on-off activation strategy to enhance control over photopolymerization. By strategically segmenting the mask and alternating the activation of adjacent segments, the technique effectively manages the distribution and concentration of free radicals and allows time for the termination phase of photopolymerization. It creates spatial gaps, helps manage free radical diffusion, prevents unwanted combinations, and filling of small gaps, thus improving resolution. Demonstrated by printing a bronchial model, this approach achieved a 100 µm gap between bronchial branches at a 14.43x compression ratio to anatomical size. The live-dead assay shows sustained cell viability of up to 90% for GelMA and up to 85% for GelMA-PEGDA constructs across seven days. This study shows that the segmentation strategy significantly reduces over-crosslinking, advancing DLP bioprinting for applications requiring precise control over complex features and detailed features. © 2025 SPIE.

关键词： Photopolymerization

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Deep Learning of Biomechanical Dynamics With Spatial Variability for Lifestyle Management 4

Deep Learning of Biomechanical Dynamics With Spatial Variabi...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Gangadharan, Kiirthanaa Zhang, Qingxue Purdue School of Engineering and Technology Department of Electrical and Computer Engineering IndianapolisIN United States Purdue School of Engineering and Technology Department of Electrical and Computer Engineering Department of Biomedical Engineering IndianapolisIN United States

ISBN: (纸本)9781665419048

Human Physical Activity Detection (PAD) is essential for advanced lifestyle management. Nowadays, many devices like smartwatches and phones have motion sensors. And the placement of devices, especially phones, is diverse. Understanding the optimal sensor location is important for robust PAD applications. However, the biomechanical dynamics are highly complex, making signal processing very challenging. In this study, we propose to leverage deep learning to perform intelligent PAD, and comprehensively compare seven different body locations for optimal sensor placement recommendation. Multi-stage deep learning has been proposed and developed, which firstly leverages convolutional layers to abstract spatial features from the motion data, and then generates the physical activity type prediction with fully connected layers. Evaluated on the real-world database, the proposed deep learning model is very effective on PAD tasks, and successfully determines the thigh location as the optimal sensor placement method. This study will greatly advance deep learning-driven biomechanical dynamics mining for advanced lifestyle management. © 2022 IEEE.

关键词： Wearable sensors

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Quaternion double random phase encoding for privacy-preserving cancelable biometrics

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Multimedia Tools and Applications 2024年第37期83卷 85065-85078页

作者： Nasr, Mahmoud Piórkowski, Adam Abd El-Samie, Fathi E. Department of Biocybernetics and Biomedical Engineering AGH University of Krakow Krakow Poland Faculty of Engineering and Technology Future University in Egypt Cairo Egypt Computer Vision Sano Centre for Computational Medicine Kraków Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Manoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Biometrics has become a fundamental aspect in the constantly-changing field of security solutions. Nevertheless, the ongoing risk of hacking and the possibility of biometric data being compromised in security breaches emphasize the urgent need for inventive and reliable biometric authentication methods. Introducing cancelable biometrics is an advanced approach to enhance the security of biometric templates. Our research paper presents a unique variation of this approach that depends on the powerful Double Random Phase Encoding (DRPE) algorithm. By incorporating an auxiliary input biometric template and using quaternion mathematics, we get an exceptional level of performance that surpasses previous performance standards. The cancelable biometric recognition system we present demonstrates exceptional performance in simulation experiments, demonstrating its ability to remain robust even in the presence of noise. More precisely, the proposed cancelable face recognition system attains a notable area under the Receiver Operating Characteristic (ROC) curve of 0.994 and an exceptionally-low Equal Error Rate (EER) of 0.00174. The efficiency of our solution is amazing, with an average time of 0.056263 seconds for each generated secure template. This demonstrates both speed and efficacy in addressing current security concerns. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Biometrics

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