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Knee deep in Error: Unraveling the accuracy limitations of consumer-grade wearable motion sensors with limited degrees of freedom computing knee joint angles

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computers in Biology and Medicine 2025年 193卷 110469页

作者： Hollingworth, Abby L. Hutchinson, Jayson B. Bechtel, Jocelynn R. Chapman, Ryan M. University of Rhode Island Department of Electrical Computer and Biomedical Engineering Fascitelli Center for Advanced Engineering 2 East Alumni Avenue KingstonRI02881 United States University of Rhode Island Department of Kinesiology 25 W. Independence Way KingstonRI02881 United States

Consumer-grade wearable inertial measurement units (IMUs) with limited degrees of freedom (DOF) are common computing knee motion before/after clinical intervention. Appropriate use requires attaching sensors per manufacturer's instructions. However, IMU misplacement error remains problematic. Thus, we assessed error computing sagittal knee angles across sensor misplacement orientations of one consumer-grade IMU system with 6DOF. We hypothesized error would increase during frontal plane misplacement. Ten young, healthy individuals (20.9 ± 0.7 years) were enrolled. IMUs were adhered/calibrated per manufacturer instructions and IMU-computed knee angles were compared to optical motion capture (MOCAP) during self-selected speed treadmill walking. Shin/thigh IMUs were then moved across sagittal, frontal, and transverse planes (±10°, ±20°). At each orientation, IMU calibration and treadmill walking was re-completed. Error was computed between MOCAP/IMU by calculating the difference and computing the absolute value (i.e. absolute value error). This was averaged across participants at each sensor position (i.e. average absolute value error). Bland Altman analyses assessed MOCAP/IMU agreement. ANOVAs assessed how plane, segment, and falsified IMU angle impacted error. MOCAP/IMUs were highly correlated with IMUs achieving clinically acceptable error ( © 2025 Elsevier Ltd

关键词： Mastication

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Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks

arXiv

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arXiv 2021年

作者： Min, Hancheng Tarmoun, Salma Vidal, René Mallada, Enrique Mathematical Institute for Data Science Johns Hopkins University United States Department of Electrical and Computer Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States

Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is to study how initialization and overparametrization affect convergence and implicit bias of training algorithms. In this paper, we present a novel analysis of single-hidden-layer linear networks trained under gradient flow, which connects initialization, optimization, and overparametrization. Firstly, we show that the squared loss converges exponentially to its optimum at a rate that depends on the level of imbalance and the margin of the initialization. Secondly, we show that proper initialization constrains the dynamics of the network parameters to lie within an invariant set. In turn, minimizing the loss over this set leads to the min-norm solution. Finally, we show that large hidden layer width, together with (properly scaled) random initialization, ensures proximity to such an invariant set during training, allowing us to derive a novel non-asymptotic upper-bound on the distance between the trained network and the min-norm solution. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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Large-Scale Extended Granger Causality for Classification of Marijuana Users from Functional MRI

arXiv

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arXiv 2021年

作者： Ali Vosoughi, M. Wismüller, Axel Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We propose large-scale Extended Granger Causality (lsXGC) and investigate whether it can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD form the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use. © 2021, CC BY.

关键词： Support vector machines

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Large-scale augmented granger causality (lsAGC) for connectivity analysis in complex systems: From computer simulations to functional MRI (fMRI)

arXiv

引用

arXiv 2021年

作者： Wismüller, Axel Vosoughi, M. Ali Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data. © 2021, CC BY.

关键词： Time series

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Alternate inter-digit finger tapping pattern resembles rudimentary finger grasping pattern

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biomedical engineering Advances 2025年 9卷

作者： Stefan Manoharan June Seung Lee Hangue Park Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA MetaBioHealth Sungkyunkwan University Suwon South Korea Department of Biomedical Engineering Sungkyunkwan University Suwon South Korea Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon South Korea

Neurotraumas often result in loss of finger grasping ability, thereby deteriorating quality of life. To develop effective rehabilitation method for finger grasping, objective evaluation metrics of grasping capability is a basic requirement. However, current evaluation methods for grasping confront challenges with their non-rhythmic nature and limited sample size, resulting in low statistical power and high variability. Finger tapping may shed light on this issue, based on its rhythmic nature and a common underlying mechanism between rhythmic finger tapping and rudimentary finger grasping. In this study, we investigated “phase difference” at inter-digit finger tapping, which indicates a deviation from the temporal symmetry of inter-digit tapping and potentially reflects a rudimentary finger grasping pattern. This was accomplished by analysing the metacarpophalangeal (MCP) angle of subjects’ fingers, measured by an infrared hand-motion sensor, during finger-tapping tasks. Subjects performed alternate or simultaneous rhythmic tapping tasks between two adjacent fingers among the thumb, index, and middle fingers while the MCP angle for the fingers was measured. Using the oscillating nature of the MCP angles for each finger, we established phase difference as a metric to describe relative interdigital motion. We identified congruency in phase difference between the alternate inter-digit tapping pattern and the rudimentary grasping pattern. Experimental results with phase analysis suggest that alternate inter-digit tapping, which is a rhythmic and intuitive task, is strongly correlated with the rudimentary grasping in kinematics, while the simultaneous tapping is not. Accordingly, we predict potential applications of alternate inter-digit finger tapping and its metrics in grasping diagnostics and rehabilitation monitoring.

关键词： Finger tapping Grasping pattern Grasping diagnostics Rehabilitation

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Implementation of MPPT technique of solar module with supervised machine learning

arXiv

引用

arXiv 2021年

作者： Sharmin, Ruhi Chowdhury, Sayeed Shafayet Abedin, Farihal Rahman, Kazi Mujibur Department of Biomedical Engineering Purdue University 206 S. Martin Jischke Drive West LafayetteIN47907-2032 United States Department of Electrical & Computer Engineering Purdue University 501 Northwestern Ave. West LafayetteIN47907-2035 United States Dhaka Bangladesh

Automated calibration of a maximum power point tracking (MPPT) algorithm and its efficacious implementation for the photo-voltaic (PV) system is pivotal for harnessing maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments and accurate analysis for solar PV systems is highly desirable. To counter this issue, numerous algorithms have been proposed so far, such as perturb and observe (P&O) method, incremental conductance (IC) method, fractional open circuit voltage (FOCV) method, short circuit current method, fuzzy logic based algorithm etc. While these approaches perform pretty well and produce overall acceptable results, recent surge of data-driven machine learning (ML) approaches hold great promise in this research domain. Since, supervised ML methods are trained directly based on the data, no human-designed heuristics are involved, which makes these techniques highly accurate and robust. As a result, in this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/ Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. To perform the task, Bayesian Regularization method was chosen as the training algorithm as it works best even for smaller data supporting the wide range of the train data set. The theoretical results show that the improved NN MPPT algorithm has higher efficiency compared with the Perturb and Observe method in the same environment, and the PV system can keep working at MPP without oscillation and probability of any kind of misjudgment. So it can not only r

关键词： Maximum power point trackers

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Adaptive thresholding skin lesion segmentation with gabor filters and principal component analysis 4th

Adaptive thresholding skin lesion segmentation with gabor fi...

引用

4th International Conference on Research in Intelligent and Computing in engineering, RICE 2019

作者： Thanh, Dang N. H. Hien, Nguyen Ngoc Surya Prasath, V.B. Erkan, Uğur Khamparia, Aditya Department of Information Technology Hue College of Industry Hue Viet Nam Center of Occupational Skills Development Dong Thap University Cao Lanh Viet Nam Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati United States Department of Pediatrics University of Cincinnati CincinnatiOH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati United States Computer Engineering Faculty of Engineering Karamanoğlu Mehmetbey University Karaman Turkey School of Computer Science and Engineering Lovely Professional University PhagwaraPunjab India

ISBN: (纸本)9789811527791

In this article, we study and propose an adaptive thresholding segmentation method for dermoscopic images with Gabor filters and Principal Component Analysis. The Gabor filters is used for extracting statistical features of image and the Principal Component Analysis is applied for transforming features to various bases. In experiments, we implement tests with the ISIC dataset. Segmentation results are assessed by the Dice and the Jaccard similarities. We also compare the proposed method to other similar methods to prove its own effectiveness. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Gabor filters

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Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

arXiv

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arXiv 2022年

作者： Yan, Rui Qu, Liangqiong Wei, Qingyue Huang, Shih-Cheng Shen, Liyue Rubin, Daniel Xing, Lei Zhou, Yuyin The Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States The Department of Statistics and Actuarial Science The Institute of Data Science The University of Hong Kong 999077 Hong Kong The Department of Biomedical Data Science Stanford University StanfordCA94305 United States The Department of Electrical Engineering Stanford University StanfordCA94305 United States The Department of Biomedical Informatics Stanford University StanfordCA94305 United States The Department of Radiation Oncology Stanford University StanfordCA94305 United States The Department of Computer Science and Engineering University of California Santa CruzCA95064 United States

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://***/rui-yan/SSL-FL. © 2022, CC BY.

关键词： Deep learning

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Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality

arXiv

引用

arXiv 2021年

作者： Wismüller, Axel Ali Vosoughi, M. Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves as a biomarker for classifying schizophrenia patients from typical controls using a subset of 62 subjects from the Centers of biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity. We cross-validate 100 different training/test (90%/10%) data split to obtain mean accuracy and a mean Area Under the receiver operating characteristic Curve (AUC) across all tested numbers of features for lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is significantly higher than the results obtained with the cross-correlation, namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our results suggest the applicability of lsXGC as a potential biomarker for schizophrenia. © 2021, CC BY.

关键词： Diseases

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Design of a robotic platform for hybrid wind tunnel experiments of floating wind farms

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Journal of Physics: Conference Series 2024年第4期2767卷 042008-042008页

作者： A. Fontanella G. Palombini A. Piffer H. Giberti M. Belloli Dept. of Mechanical Engineering Politecnico di Milano via La Masa 1 20156 Milano Italy. Dept. of Electrical Computer and Biomedical Engineering Università degli Studi di Pavia via Adolfo Ferrata 5 27100 Pavia Italy.

Wind tunnel experiments incorporating factors like realistic ambient wind conditions, merging of multiple wakes, and active wake controls are needed to understand and improve modeling of floating wind farms. A key technology for this kind of experiments is the robotic system emulating the wind turbine motion. This article addresses the design of a robotic platform with three degrees-of-freedom (surge, pitch, and yaw) specifically tailored for wind tunnel experiments on floating wind farms. This robotic system aims to accurately reproduce the motion spectrum of floating wind turbines of 10-22MW and to simulate rotor-atmospheric wind interactions. The robotic platform has a compact design to be positioned in multiple units inside the wind tunnel and avoid disturbing the wake of the wind turbine on top of it. To achieve these goals, the wind turbine is partially integrated in the robotic platform that employs a parallel kinematic scheme and has all actuators close to the wind tunnel floor.

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